CN117597742A

CN117597742A - Digital measurement stack for characterizing a disease, measuring an intervention effect or determining a result

Info

Publication number: CN117597742A
Application number: CN202280047843.XA
Authority: CN
Inventors: K·兰格尔; B·哈托格; E·德贝克拉尔
Original assignee: Janssen Pharmaceutica NV
Current assignee: Janssen Pharmaceutica NV
Priority date: 2021-05-06
Filing date: 2022-05-06
Publication date: 2024-02-23

Abstract

Disclosed herein are standardized digital solutions, such as Target Solution Profiles (TSPs) and Digital Measurement Solutions (DMS), that can be used to characterize a subject's disease. Typically, TSPs and DMS are made up of a measurement stack comprising a plurality of elements. The development of these standardized solutions for various diseases enables coordination among parties (e.g., parties interested in characterizing the disease that participate in clinical trials). Furthermore, in view of the evolving situation of new devices and new software, standardized solutions help to improve lifecycle management. Furthermore, these digital measurement solutions represent novel solutions for characterizing diseases.

Description

Digital measurement stack for characterizing a disease, measuring an intervention effect or determining a result

Cross Reference to Related Applications

The present application claims the benefit and priority of U.S. provisional patent application No. 63/184,907 filed on 5 months 6 of 2021 and EP21383036.7 filed on 11 months 16 of 2021, the complete disclosures of each of these patents being hereby incorporated by reference in their entirety for all purposes.

Background

Digital health techniques show high development potential in real-world evidence data generation. In the last decade, the number of clinical trials involving digital health techniques showed a 34.1% increase in composite years. However, to date, multiple limiting factors have prevented digital health technologies from being used by humans. Frequently mentioned limiting factors include: (1) lack of standardization process, (2) there are various concerns about how to select the most suitable digital metrics, (3) how to collect, analyze and interpret captured real world evidence, (4) in view of the ever changing technology, it is difficult to maintain the integrity of the solution, (5) how to prepare supporting materials for submitting regulatory approval, and (6) lack of conversion means from conception to practical application in clinical research and clinical care.

Disclosure of Invention

Disclosed herein are methods, systems, and non-transitory computer-readable media for constructing, implementing, and providing standardized digital solutions, such as Target Solution Profiles (TSPs) and Digital Measurement Solutions (DMS). In general, the DMS specifies elements of the complete solution (e.g., specific devices, algorithms, and details of the measurement solution), while the TSP represents a measurement method that describes how the different elements interact. These TSPs and DMS may be used to characterize a disease in a subject, and may be provided to third parties to enable these third parties to characterize the disease. TSPs and DMS are typically made up of a measurement stack comprising multiple layers (also referred to herein as "elements"). The elements are connected to adjacent elements in the measurement stack and each element is useful for approved applications of the digital measurement solution. In various embodiments, the specific elements of TSP and DMS are specifically developed for or specific to a particular disease or group (sub) of patients suffering from a particular disease. In various embodiments, certain elements of the DMS are interchangeable, and the DMS can be swapped in and out for various diseases.

Generally, digital Measurement Solutions (DMS) are divided into generic object solution profiles (TSPs). Thus, various DMS may belong to a common class represented by TSP. TSPs are intended to fill the previously mentioned standardized gaps by describing agnostic classifications (e.g., device agnostic, device-software agnostic, algorithm agnostic). Thus, TSP represents a class of standardized solutions with consistent definition and verified instrumentation.

In summary, TSP and DMS represent standardized solutions for characterizing diseases, as described herein. Examples of characterizing a disease include, but are not limited to, determining the severity of the disease, determining the likelihood of disease progression, and measuring the outcome of treatment of the disease. A first particular benefit is that the TSP allows for coordinated coordination between multiple resources and elements, thereby improving standardization within the ecosystem. Second, development time of standardized solutions (e.g., DMS) is significantly shortened, which allows for resource savings and reduces unnecessary costs. For example, the available elements or resources can be readily reused for similar or identical conditions. Third, TSPs allow for improved lifecycle management. Qualification protocols were developed for individual TSPs that cover various DMS. In one scenario, this ensures that DMS in the public class represented by the TSP performs in a similar or comparable manner. In a second scenario, when an upgrade occurs (e.g., when a new measurement instrument is developed or when a new software algorithm is available), the DMS can be effectively evaluated using a qualification protocol to ensure that the DMS's solution is comparable within the solution class. Thus, the introduction of TSP and DMS into the ecosystem, first in clinical studies, and also in clinical care, accelerates the adoption of digital metrics and long-term study interoperability (e.g., interoperability across different clinical trials).

Disclosed herein are methods for characterizing a disease in a subject, the method comprising: obtaining a measurement of interest from a subject; selecting a digital measurement solution from a plurality of digital measurement solutions, wherein the plurality of digital measurement solutions belong to a common category represented by a target solution profile; and applying the selected digital measurement solution to the obtained measurement of interest to characterize the disease of the subject, wherein the digital measurement solution comprises: a measurement definition defining one or more concepts of interest related to a disease; a surveying instrument resource that transforms a measurement of interest captured according to a measurement definition into a dataset providing information for characterizing a disease, wherein the surveying instrument resource of the digital measurement solution is specific to a device used to capture the measurement of interest; and optionally, an evidence resource for performing one or more verifications on the data set generated by the measurement instrument resource, wherein the target solution profile does not change over time and enables effective lifecycle management of the plurality of digital measurement solutions. In various embodiments, the target solution profile represents generalization of a plurality of digital measurement solutions, wherein the measurement instrument resources of the target solution profile are device technology agnostic. In various embodiments, performing one or more verifications includes performing one or more of a technical, analytical, or clinical verification. In various embodiments, performing technical verification includes comparing a dataset generated by a measurement instrument resource to specifications of one or more devices used to capture measurements of interest. In various embodiments, performing analytical verification includes: determining any of reliability, specificity, or sensitivity indicators of the data set; and comparing the reliability, specificity, or sensitivity index to a threshold. In various embodiments, performing the clinical validation comprises: the therapeutic effect of the measurement of interest on the disease is evaluated.

In various embodiments, the digital measurement solutions are pre-validated by implementing one or more qualification protocols for establishing solution comparability between the digital measurement solutions of the target solution profile. In various embodiments, the qualification protocol includes the steps of: a) Recruiting an N member participant group; b) Capturing measurements of interest throughout the N-member participant group according to specifications of the digital measurement solution; c) According to the specification, transforming the measurement of interest into a dataset; and d) validating the data set to determine whether the digital measurement solution implements a comparable solution to the target solution profile. In various embodiments, validating the data set includes: determining whether a characteristic of the dataset meets a threshold for a target solution profile; and in response to determining that the characteristic of the dataset meets a threshold, validating the digital measurement solution as implementing the comparability of the solution. In various embodiments, validating the data set further includes storing an indication of successful validation in metadata of the digital measurement solution in response to determining a comparability of the digital measurement solution implementation. In various embodiments, the metadata of the digital measurement solution is stored in a directory accessible by third party users for review. In various embodiments, the specification of the digital measurement solution represents upgrade capabilities compared to a previous version of the digital measurement solution. In various embodiments, the specification of the digital measurement solution represents upgrade capabilities included in a new distribution device for capturing measurement results of interest. In various embodiments, the upgrade capability is one of upgrading a battery, upgrading a data store, upgrading a collection frequency, or upgrading a data collection algorithm. In various embodiments, the common category of the plurality of digital measurement solutions represents a common method of measuring activity from an individual. In various embodiments, common methods of measuring activity use a class of devices including one or more of wearable devices, devices including accelerometers, devices including gyroscopes, ingestible devices, image and voice based devices, non-contact sensors, and sensor-based smart devices (e.g., balances, thermometers, and respirators). In various embodiments, the measurement instrument resource includes a machine learning algorithm that transforms data captured according to a measurement definition into a dataset.

Further disclosed herein is a method for constructing a digital measurement solution for characterizing a disease, the method comprising: generating a measurement definition of the target solution profile, the measurement definition defining one or more concepts of interest related to the disease; generating or selecting a measurement instrument resource for the target solution profile, the measurement instrument resource configured to transform data captured according to the measurement definition into a dataset, the measurement instrument resource being device technology agnostic and thereby interchangeable between different target solution profiles; generating an evidence resource of the target solution profile for performing one or more verifications on the data set generated by the measurement instrument resource; generating a digital measurement solution by means of at least a device specifying a measurement instrument resource for a target solution profile, wherein the digital measurement solution belongs to a common category represented by the target solution profile, wherein the target solution profile does not change over time and thereby enables an efficient lifecycle management of a plurality of digital measurement solutions.

In various embodiments, the one or more concepts of interest associated with the disease include medical measurements of the disease, or a measurable experience of an individual suffering from the disease. In various embodiments, device technology agnostic includes one or both of device agnostic and device version agnostic. In various embodiments, the methods disclosed herein further include implementing a qualification protocol for establishing solution comparability between a plurality of digital measurement solutions of the target solution profile to verify the digital measurement solution. In various embodiments, the qualification protocol includes the steps of: a) Recruiting an N member participant group; b) Capturing measurements of interest throughout the N-member participant group using a specification of a digital measurement solution; c) According to the specification, transforming the measurement of interest into a dataset; and d) validating the data set to determine whether the digital measurement solution implements a comparable solution to the target solution profile. In various embodiments, validating the data set includes: determining whether the data set meets a threshold of a target solution profile; and in response to determining that the data set meets a threshold, validating the digital measurement solution as achieving comparability of the solution. In various embodiments, validating the data set further includes storing an indication of successful validation in metadata of the digital measurement solution in response to determining a comparability of the digital measurement solution implementation. In various embodiments, the metadata of the digital measurement solution is stored in a directory accessible by third party users for review. In various embodiments, the specification of the digital measurement solution represents the upgrade capability of a previous version of the digital measurement solution. In various embodiments, the specification of the digital measurement solution represents upgrade capabilities included in a new distribution device for capturing measurement results of interest. In various embodiments, the upgrade capability is one of upgrading a battery, upgrading a data store, upgrading a collection frequency, or upgrading a data collection algorithm.

In various embodiments, the measurement definition and evidence resources are fixed for the target solution profile and specific to a disease. In various embodiments, the measurement instrument resources of the target solution profiles are interchangeable between different target solution profiles for characterizing the same disease or different diseases. In various embodiments, the measurement instrument resource is specific to a common method of measuring an individual's activity. In various embodiments, common methods of measuring activity use a class of devices including one or more of wearable devices, devices including accelerometers, devices including gyroscopes, ingestible devices, image and voice based devices, non-contact sensors, and sensor-based smart devices (e.g., balances, thermometers, and respirators). In various embodiments, the digital measurement solution includes providing the digital measurement solution to a third party for regulatory approval. In various embodiments, the digital measurement solution includes providing input to a third party regarding one or more resources of the digital measurement solution.

In various embodiments, the methods disclosed herein further comprise providing the digital measurement solution to a third party for regulatory approval. In various embodiments, the methods disclosed herein further comprise providing input to a third party regarding one or more resources of the digital measurement solution.

In various embodiments, the digital measurement solution is one of the digital measurement solutions shown in table 5. In various embodiments, the target solution profile is one of the target solution profiles shown in table 4. In various embodiments, the disease is a disorder shown in table 1. In various embodiments, the one or more concepts of interest are selected from the concepts of interest shown in table 3.

Further disclosed herein is a method for providing one or more digital measurement solutions useful for characterizing a disease, the method comprising: providing a directory comprising a plurality of target solution profiles, wherein each of the one or more target solution profiles comprises: a measurement definition of a target solution profile defining one or more concepts of interest related to a disease; a measurement instrument resource that transforms the measurement results of interest captured according to the measurement definition into a dataset providing information for characterizing the disease, the measurement instrument resource being device technology agnostic and thereby interchangeable between different target solution profiles; and an evidence resource for performing one or more verifications on the data set generated by the measurement instrument resource; receiving a selection of one of the target solution profiles from a third party; and providing the one or more digital measurement solutions to the third party that are useful for characterizing the disease, wherein the one or more digital measurement solutions belong to a common category represented by the selected target solution profile.

In various embodiments, the methods disclosed herein further comprise: receiving a search query from a third party; for each of one or more target solution profiles of a plurality of target solution profiles, evaluating the target solution profile to determine whether the target solution profile satisfies a query; and returning a list of target solution profiles that satisfy the query. In various embodiments, evaluating the target solution profile includes: one or more measurement definition elements that satisfy the concept of interest of the query are evaluated. In various embodiments, the methods disclosed herein further comprise: replacing the measurement instrument resource of one of the one or more digital measurement solutions with a second measurement instrument resource to generate a revised digital measurement solution; and providing the revised digital measurement solution to the third party. In various embodiments, the methods disclosed herein further comprise receiving, from a third party, a suggested target solution profile that is not present in the provided catalog; and further generating a suggested target solution profile for inclusion in the catalog.

In various embodiments, the target solution profile includes a measurement stack that includes a plurality of elements. In various embodiments, each of the measurement definition, the measurement instrument resource, and the evidence resource is represented by one or more elements of the plurality of elements. In various embodiments, the sequence of elements includes one or more measurement definition elements, followed by one or more measurement instrument resource elements, followed by one or more evidence resource elements. In various embodiments, the measurement definition element interfaces with a measurement instrument resource element that interfaces with an evidence resource. In various embodiments, the one or more measurement definition elements include one or more of disease hypotheses and measurable concepts of interest. In various embodiments, the one or more measurement instrument resource elements include one or more of measurement methods, raw data, and machine learning algorithms. In various embodiments, the one or more evidence resource elements include one or more of technical, analytical, and clinical validations. In various embodiments, the one or more measurement definition elements include disease hypotheses and measurable concepts of interest, wherein the one or more measurement instrument resource elements include measurement methods, raw data, and machine learning algorithms, and wherein the one or more evidence resource elements include technical, analytical, and clinical validations. In various embodiments, the plurality of elements of the measurement stack are a plurality of layers.

In various embodiments, the disease is dementia. In various embodiments, it is hypothesized that intervention means to slow the progression of dementia are included. In various embodiments, the measurable concepts of interest include one or more of attention, language, or executive function. In various embodiments, the measurement method includes a method for capturing speech. In various embodiments, the raw data comprises raw speech data captured from a subject, or magnetic resonance imaging data. In various embodiments, the machine learning algorithm includes one or more of a natural language processing algorithm, a clustering algorithm, and a clinical variable predictor. In various embodiments, the analytical verification includes evidence verification of machine learning algorithm performance. In various embodiments, the clinical interpretation includes evidence supporting disease characterization according to clinical dementia ratings. In various embodiments, the disease is parkinson's disease. In various embodiments, it is hypothesized that interventions to reduce tremors are included. In various embodiments, the measurable concept of interest includes the ability to perform moderate-intensity daily activities. In various embodiments, the measurement method includes a method of capturing physiological data using a biosensor. In various embodiments, the raw data includes raw physiological data captured using a biosensor. In various embodiments, the machine learning algorithm transforms the raw data into a dataset. In various embodiments, the analytical verification includes evidence verification of machine learning algorithm performance. In various embodiments, the clinical interpretation includes evidence that supports disease characterization in a population of parkinson's disease patients.

In various embodiments, the disease is atopic dermatitis. In various embodiments, it is assumed that intervention means to reduce nocturnal scratching are included. In various embodiments, the measurable concept of interest includes nocturnal scratching. In various embodiments, the measurement method includes a method of capturing physiological data using a wearable device. In various embodiments, the raw data includes raw physiological data captured using a wearable device. In various embodiments, the machine learning algorithm transforms the raw data into a dataset. In various embodiments, the clinical verification includes evidence to support the therapeutic effects of nocturnal scratching for the atopic dermatitis population.

In various embodiments, the disease is pulmonary arterial hypertension. In various embodiments, it is hypothesized to include intervention means that improve the ability of the patient to conduct physical activity after treatment. In various embodiments, the measurable concept of interest includes the daily performance of activities of the patient affected by pulmonary arterial hypertension. In various embodiments, the measurement method includes a wrist-worn device for capturing physiological data. In various embodiments, the raw data includes raw physiological data measured from sensors of the wrist-worn device, wherein the sensors include one or more of an accelerometer, a gyroscope, and a magnetometer. In various embodiments, the machine learning algorithm transforms the raw data into a dataset. In various embodiments, the clinical verification includes evidence of improvement in daily performance following intervention.

Additionally disclosed herein is a non-transitory computer-readable medium for characterizing a disease in a subject, the non-transitory computer-readable medium comprising instructions that, when executed by a processor, cause the processor to: obtaining a measurement of interest from a subject; selecting a digital measurement solution from a plurality of digital measurement solutions, wherein the plurality of digital measurement solutions belong to a common category represented by a target solution profile; and applying the selected digital measurement solution to the obtained measurement of interest to characterize the disease of the subject, wherein the digital measurement solution comprises: a measurement definition defining one or more concepts of interest related to a disease; a surveying instrument resource that transforms a measurement of interest captured according to a measurement definition into a dataset providing information for characterizing a disease, wherein the surveying instrument resource of the digital measurement solution is specific to a device used to capture the measurement of interest; and optionally, an evidence resource for performing one or more verifications on the data set generated by the measurement instrument resource, wherein the target solution profile does not change over time and enables effective lifecycle management of the plurality of digital measurement solutions. In various embodiments, the target solution profile represents generalization of a plurality of digital measurement solutions, wherein the measurement instrument resources of the target solution profile are device technology agnostic.

In various embodiments, the instructions that cause the processor to perform one or more validations further include instructions that, when executed by the processor, cause the processor to: one or more of technical, analytical, or clinical verification is performed. In various embodiments, the instructions that cause the processor to perform technical verification further include instructions that, when executed by the processor, cause the processor to compare the data set generated by the measurement instrument resource to specifications of one or more devices for capturing the measurement result of interest. In various embodiments, the instructions that cause the processor to perform analytical verification further include instructions that, when executed by the processor, cause the processor to perform any of a reliability, specificity, or sensitivity indicator of the data set; and comparing the reliability, specificity, or sensitivity index to a threshold. In various embodiments, the instructions that cause the processor to perform clinical validation further include instructions that, when executed by the processor, cause the processor to evaluate the therapeutic effect of the measurement of interest on the disease. In various embodiments, the digital measurement solutions are pre-validated by implementing one or more qualification protocols for establishing solution comparability between the digital measurement solutions of the target solution profile. In various embodiments, the qualification protocol includes the steps of: a) Recruiting an N member participant group; b) Capturing measurements of interest throughout the N-member participant group according to specifications of the digital measurement solution; c) According to the specification, transforming the measurement of interest into a dataset; and d) validating the data set to determine whether the digital measurement solution implements a comparable solution to the target solution profile. In various embodiments, validating the data set includes: determining whether a characteristic of the dataset meets a threshold for a target solution profile; and in response to determining that the characteristic of the dataset meets a threshold, validating the digital measurement solution as implementing the comparability of the solution. In various embodiments, validating the data set further includes storing an indication of successful validation in metadata of the digital measurement solution in response to determining a comparability of the digital measurement solution implementation. In various embodiments, the metadata of the digital measurement solution is stored in a directory accessible by third party users for review. In various embodiments, the specification of the digital measurement solution represents upgrade capabilities compared to a previous version of the digital measurement solution. In various embodiments, the specification of the digital measurement solution represents upgrade capabilities included in a new distribution device for capturing measurement results of interest. In various embodiments, the upgrade capability is one of upgrading a battery, upgrading a data store, upgrading a collection frequency, or upgrading a data collection algorithm. In various embodiments, the common category of the plurality of digital measurement solutions represents a common method of measuring activity from an individual. In various embodiments, common methods of measuring activity use a class of devices including one or more of wearable devices, devices including accelerometers, devices including gyroscopes, ingestible devices, image and voice based devices, non-contact sensors, and sensor-based smart devices (e.g., balances, thermometers, and respirators). In various embodiments, the measurement instrument resource includes a machine learning algorithm that transforms data captured according to a measurement definition into a dataset.

Additionally disclosed herein is a non-transitory computer-readable medium for constructing a data measurement solution characterizing a disease, the non-transitory computer-readable medium comprising instructions that, when executed by a processor, cause the processor to: generating a measurement definition of the target solution profile, the measurement definition defining one or more concepts of interest related to the disease; generating or selecting a measurement instrument resource for the target solution profile, the measurement instrument resource configured to transform data captured according to the measurement definition into a dataset, the measurement instrument resource being device technology agnostic and thereby interchangeable between different target solution profiles; generating an evidence resource of the target solution profile for performing one or more verifications on the data set generated by the measurement instrument resource; generating a digital measurement solution by means of at least a device specifying a measurement instrument resource for a target solution profile, wherein the digital measurement solution belongs to a common category represented by the target solution profile, wherein the target solution profile does not change over time and thereby enables an efficient lifecycle management of a plurality of digital measurement solutions.

In various embodiments, the one or more concepts of interest associated with the disease include medical measurements of the disease, or a measurable experience of an individual suffering from the disease. In various embodiments, device technology agnostic includes one or both of device agnostic and device version agnostic. In various embodiments, the non-transitory computer-readable medium further includes instructions that, when executed by the processor, cause the processor to implement a qualification protocol for verifying digital measurement solutions, the qualification protocol for establishing solution comparability between a plurality of digital measurement solutions of the target solution profile. In various embodiments, the qualification protocol includes the steps of: a) Recruiting an N member participant group; b) Capturing measurements of interest throughout the N-member participant group using a specification of a digital measurement solution; c) According to the specification, transforming the measurement of interest into a dataset; and d) validating the data set to determine whether the digital measurement solution implements a comparable solution to the target solution profile. In various embodiments, validating the data set includes: determining whether the data set meets a threshold of a target solution profile; and in response to determining that the data set meets a threshold, validating the digital measurement solution as achieving comparability of the solution. In various embodiments, validating the data set further includes storing an indication of successful validation in metadata of the digital measurement solution in response to determining a comparability of the digital measurement solution implementation. In various embodiments, the metadata of the digital measurement solution is stored in a directory accessible by third party users for review. In various embodiments, the specification of the digital measurement solution represents the upgrade capability of a previous version of the digital measurement solution. In various embodiments, the specification of the digital measurement solution represents upgrade capabilities included in a new distribution device for capturing measurement results of interest. In various embodiments, the upgrade capability is one of upgrading a battery, upgrading a data store, upgrading a collection frequency, or upgrading a data collection algorithm.

In various embodiments, the measurement definition and evidence resources are fixed for the target solution profile and specific to a disease. In various embodiments, the measurement instrument resources of the target solution profiles are interchangeable between different target solution profiles for characterizing the same disease or different diseases. In various embodiments, the measurement instrument resource is specific to a common method of measuring an individual's activity. In various embodiments, common methods of measuring activity use a class of devices including one or more of wearable devices, devices including accelerometers, devices including gyroscopes, ingestible devices, image and voice based devices, non-contact sensors, and sensor-based smart devices (e.g., balances, thermometers, and respirators). In various embodiments, the digital measurement solution is one of the digital measurement solutions shown in table 5. In various embodiments, the target solution profile is one of the target solution profiles shown in table 4. In various embodiments, the disease is a disorder shown in table 1. In various embodiments, the one or more concepts of interest are selected from the concepts of interest shown in table 3.

Additionally disclosed herein is a non-transitory computer-readable medium for providing one or more digital measurement solutions useful for characterizing a disease, the non-transitory computer-readable medium comprising instructions that, when executed by a processor, cause the processor to: providing a directory comprising a plurality of target solution profiles, wherein each of the one or more target solution profiles comprises: a measurement definition of a target solution profile defining one or more concepts of interest related to a disease; a measurement instrument resource that transforms the measurement results of interest captured according to the measurement definition into a dataset providing information for characterizing the disease, the measurement instrument resource being device technology agnostic and thereby interchangeable between different target solution profiles; and an evidence resource for performing one or more verifications on the data set generated by the measurement instrument resource; receiving a selection of one of the target solution profiles from a third party; and providing the one or more digital measurement solutions to the third party that are useful for characterizing the disease, wherein the one or more digital measurement solutions belong to a common category represented by the selected target solution profile. In various embodiments, the non-transitory computer-readable medium disclosed herein further includes instructions that, when executed by the processor, cause the processor to: receiving a search query from a third party; for each of one or more target solution profiles of a plurality of target solution profiles, evaluating the target solution profile to determine whether the target solution profile satisfies a query; and returning a list of target solution profiles that satisfy the query. In various embodiments, the instructions that cause the processor to evaluate the target solution profile further comprise instructions that, when executed by the processor, cause the processor to evaluate one or more measurement definition elements that satisfy the concept of interest of the query. In various embodiments, the non-transitory computer-readable medium disclosed herein further includes instructions that, when executed by the processor, cause the processor to replace the measurement instrument resource of one of the one or more digital measurement solutions with the second measurement instrument resource to generate a revised digital measurement solution; and providing the revised digital measurement solution to the third party.

In various embodiments, the non-transitory computer-readable medium disclosed herein further includes instructions that, when executed by the processor, cause the processor to: receiving a suggested target solution profile from a third party that is not present in the provided catalog; and further generating a suggested target solution profile for inclusion in the catalog.

In various embodiments, the disease is dementia. In various embodiments, it is hypothesized that intervention means to slow the progression of dementia are included. In various embodiments, the measurable concepts of interest include one or more of attention, language, or executive function. In various embodiments, the measurement method includes a method for capturing speech. In various embodiments, the raw data comprises raw speech data captured from a subject, or magnetic resonance imaging data. In various embodiments, the machine learning algorithm includes one or more of a natural language processing algorithm, a clustering algorithm, and a clinical variable predictor. In various embodiments, the analytical verification includes evidence verification of machine learning algorithm performance. In various embodiments, the clinical interpretation includes evidence supporting disease characterization according to clinical dementia ratings.

In various embodiments, the disease is parkinson's disease. In various embodiments, it is hypothesized that interventions to reduce tremors are included. In various embodiments, the measurable concept of interest includes the ability to perform moderate-intensity daily activities. In various embodiments, the measurement method includes a method of capturing physiological data using a biosensor. In various embodiments, the raw data includes raw physiological data captured using a biosensor. In various embodiments, the machine learning algorithm transforms the raw data into a dataset. In various embodiments, the analytical verification includes evidence verification of machine learning algorithm performance. In various embodiments, the clinical interpretation includes evidence that supports disease characterization in a population of parkinson's disease patients.

Drawings

These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description and accompanying drawings. It should be noted that wherever possible, similar or identical reference numbers may be used in the drawings and may represent similar or identical functions. For example, a letter (such as "third party entity 110A") following a reference numeral indicates that the text specifically refers to an element having that particular reference numeral. Reference numerals without letters followed in the text, such as "third party entity 110", refer to any or all elements in the drawing having the reference numerals (e.g., "third party entity 110" in the text refers to reference numerals "third party entity 110A" and/or "third party entity 110A" in the drawing).

Fig. 1A is a system overview including a digital solution system and one or more third party entities, according to one embodiment.

Fig. 1B is a block diagram of a digital solution system according to one embodiment.

FIG. 2A is an example measurement stack according to one embodiment.

FIG. 2B is an example measurement stack showing various elements, according to one embodiment.

Fig. 2C is an example measurement stack indicating one or more elements forming a Target Solution Profile (TSP), a Target Instrumentation Profile (TIP), or a target element profile (TCP), according to one embodiment.

FIG. 2D illustrates an example target solution profile according to one embodiment.

Fig. 2E shows an example digital measurement solution according to the first embodiment.

Fig. 2F shows an example digital measurement solution according to a second embodiment.

FIG. 3A is an example flow for constructing a digital measurement solution, according to one embodiment.

Fig. 3B is an example flow for characterizing a subject's disease using a Digital Measurement Solution (DMS), according to one embodiment.

FIG. 3C is an example flow for providing a target solution profile or one or more digital measurement solutions, according to one embodiment.

Fig. 4 illustrates an example computing device for implementing the systems and methods described in fig. 1A-1B, 2A-2F, and 3A-3C.

Fig. 5A depicts an example target solution profile for atopic dermatitis.

Fig. 5B depicts an example digital measurement solution for atopic dermatitis.

Fig. 5C depicts the resource interchangeability of different digital measurement solutions for atopic dermatitis.

Fig. 6A depicts an example target solution profile for pulmonary arterial hypertension.

Fig. 6B depicts a first example digital measurement solution for pulmonary arterial hypertension.

Fig. 6C depicts a second example digital measurement solution for pulmonary arterial hypertension.

Fig. 6D depicts at least reuse of measurement instrument resources in a digital measurement solution.

Fig. 7 depicts an example digital measurement solution for parkinson's disease.

FIG. 8A depicts a high-level overview relating to collaborative effort for developing standardized solutions.

Fig. 8B depicts an example flow involving parties for dynamic supervision assessment implementing a standardized solution.

Detailed Description

Definition of the definition

Unless otherwise indicated, the terms used in the claims and the specification are defined as set forth below.

The term "subject" or "patient" is used interchangeably and encompasses cells, tissues, organisms, humans or non-humans, mammals or non-mammals, male or female, whether in vivo, ex vivo, or in vitro.

The terms "disease" or "disorder" are used interchangeably and generally refer to a diseased state of a subject. Generally, standardized solutions (such as digital measurement solutions) are implemented to characterize a subject's disease.

The phrase "measurement stack" refers to an organized form of one or more resources made up of elements. In particular embodiments, the measurement stack is made up of two or more resources. In particular embodiments, the measurement stack is made up of three or more resources. For example, the measurement stack includes a measurement definition resource, a measurement instrument resource, and an evidence resource. The measurement stack provides a structure for standardized solutions, such as target solution profiles or digital measurement solutions.

The phrase "target solution profile" or "TSP" refers to such a measurement stack: wherein the generic description is incorporated to provide device technology agnostic profiles (e.g., profiles that are independent of a particular hardware device and/or that are independent of particular software). In various implementations, the target solution profile includes each of a measurement definition resource, a measurement instrument resource, and an evidence resource. In various embodiments, the instrumentation resources of the target solution profile describe a general method of capturing and transforming the raw data of interest, but do not specify specific devices and algorithms for capturing and transforming the raw data. The target solution profile represents a common class of digital measurement solutions. The target solution profile may specify performance requirements and/or criteria such that the common class of digital measurement solutions represented by the target solution profile are evaluated and validated to function within the performance requirements and/or criteria.

The phrase "digital measurement solution" or "DMS" refers to a specific digital solution built on a measurement stack. In various embodiments, the DMS specifies all elements of the complete solution, which may include devices, algorithms, external data, definitions, and/or evidence. For example, a digital measurement solution identifies a specific device or software for capturing raw data. In various embodiments, the digital measurement solution identifies a specific algorithm for transforming raw data into meaningful health data. Thus, implementing a digital measurement solution may be used to characterize a disease in a subject.

The phrase "standardized solution" refers to a standard digital solution that can be used to characterize a disease. Examples of standardized solutions include digital measurement solutions and target solution profiles.

SUMMARY

Fig. 1A is a system overview including a digital solution system 130 and one or more third party entities 110, according to one embodiment. In particular, FIG. 1A illustrates a digital solution system 130 connected to one or more third party entities 110 via a network 120. Although fig. 1A depicts digital solution system 130 connected to two separate third party entities 110A and 110B, in various embodiments digital solution system 130 may be connected to additional third party entities (e.g., tens, hundreds, or even thousands of third party entities).

Generally, the digital solution system 130 builds and/or maintains standardized solutions, examples of which include Digital Measurement Solutions (DMS) and Target Solution Profiles (TSPs) built on a measurement stack. Standardized solutions can be used to characterize a subject's disease, and in addition, enable effective lifecycle management for various solutions. In various embodiments, the digital solution system 130 interacts with various third party entities (e.g., third party entities 110A and/or 110B) to build and maintain DMS and TSPs. In various embodiments, the digital solution system 130 represents a centralized marketplace that incorporates these standardized DMS and TSPs. Thus, the digital solution system 130 provides standardized DMS and TSPs to third party entities (e.g., third party entities 110A and/or 110B) via a centralized marketplace so that the third party entities can use these standardized solutions, for example, to characterize a disease or disorder.

Third party entity

In various embodiments, the third party entity 110 represents a partner entity of the digital solution system 130. In some embodiments, the third party entity 110 is a partner entity that cooperates with the digital solution system 130 to build TSPs and/or DMS. In some scenarios, the third party entity 110 represents a resource developer. As one example, the third party entity 110 may develop elements that can be provided to the digital solution system 130 for incorporation into a standardized solution (e.g., DMS or TSP). As another example, the third party entity 110 may provide feedback to the digital solution system 130. For example, the third party entity 110 may provide suggestions about valuable standardized solutions (e.g., DMS or TSP). These standardized solutions may currently be missing (e.g., not present in the catalog, or not available in the marketplace). Thus, the digital solution system 130 can generate these suggested standardized solutions, perform appropriate verification, and then include them in the marketplace.

In some embodiments, the third party entity 110 represents a regulatory expert. Here, the third party entity 110 may interact with the digital solution system 130 to verify standardized solutions (e.g., DMS and TSP) and approve them as standard solutions for clinical trials. As described in further detail herein, DMS and TSP may include elements that provide specific guidelines for regulatory experts, which may improve the standardization and adoption rate of these solutions.

In various embodiments, multiple third party entities 110 cooperate together to build standardized solutions and to implement regulatory acceptance of those standardized solutions. For example, multiple third party entities 110 cooperate together to enable dynamic regulatory assessment of standardized solutions (e.g., DMS and/or TSPs). In various embodiments, the plurality of third party entities 110 includes stakeholders interested in building standardized solutions. Such stakeholders may include resource developers (e.g., entities that build and/or provide elements and/or resources), pharmaceutical companies, observers, service providers, and/or customers (e.g., entities interested in using standardized solutions). Thus, these stakeholders may provide feedback when working together to build a standardized solution. In various embodiments, the plurality of third party entities 110 further includes a supervising individual performing a supervising assessment of the standardized solutions. Thus, these regulatory individuals may provide regulatory acceptance of standardized solutions. In various embodiments, these supervising individuals may interact with other multiple third party entities 110 (e.g., stakeholders) to enable dynamic supervising assessment. For example, the supervising individual may agree with stakeholders in understanding the context and use cases of the standardized solutions, thereby ensuring that regulatory approval is achieved more quickly.

In some embodiments, the third party entity 110 represents a customer interested in accessing and using standardized solutions (such as DMS) to characterize a subject's disease. Example clients include any of sponsors (e.g., clinical trial sponsors), clinical researchers, healthcare professionals, doctors, sellers, or suppliers. In such embodiments, the third party entity 110 may interact with the digital solution system 130 to access and use the standardized solutions. For example, the digital solution system 130 may provide the third party entity 110 with a TSP and/or DMS that is appropriate for the needs of the third party entity 110. For example, as described in further detail herein, the DMS and TSP may identify particular specifications (e.g., device specifications or software specifications) that establish a measurement of interest captured for a particular disease or disorder (e.g., captured from a subject with or without the disease or disorder). Thus, a third party entity 110 interested in characterizing a particular disease or condition may evaluate the required specifications and then identify the appropriate DMS or TSP that best meets its needs. The digital solution system 130 may provide an appropriate DMS or TSP. Using the appropriate DMS, the third party entity 110 characterizes the disease of one or more subjects. For example, the third party entity 110 may capture measurements of interest from the subject according to the measurement method described in the DMS. The third party entity 110 may also transform the measurement results of interest into meaningful health data using algorithms specified in the DMS. The third party entity 110 then interprets the meaningful health data and characterizes the disease.

Network system

The present disclosure contemplates any suitable network 120 capable of enabling a connection between digital solution system 130 and third party entity 702. Network 120 may include any combination of local area and/or wide area networks using wired and/or wireless communication systems. In one embodiment, network 120 uses standard communication techniques and/or protocols. For example, network 120 includes communication links using technologies such as Ethernet, 802.11, worldwide Interoperability for Microwave Access (WiMAX), 3G, 4G, code Division Multiple Access (CDMA), digital Subscriber Line (DSL), and the like. Examples of networking protocols for communicating via network 120 include multiprotocol label switching (MPLS), transmission control protocol/internet protocol (TCP/IP), hypertext transfer protocol (HTTP), simple Mail Transfer Protocol (SMTP), and File Transfer Protocol (FTP). Data exchanged over network 120 may be represented using any suitable format, such as hypertext markup language (HTML) or extensible markup language (XML). In some embodiments, all or some of the communication links of network 120 may be encrypted using any suitable technique or techniques.

Digital solution system

Fig. 1B is a block diagram of a digital solution system 130 according to one embodiment. FIG. 1B is presented to introduce elements of the digital solution system 130, including a resource module 140, a target solution profile module 145, a Digital Measurement Solution (DMS) module 150, a qualification protocol module 155, a disease characterization module 160, and a market module 165. The digital solution system 130 may further include a data store, such as an element store 170, a target solution profile store 175, and a Digital Measurement Solution (DMS) store 180. In various embodiments, digital solution system 130 may include additional elements or need not include all of the elements shown in fig. 1B. For example, the disease characterization module 160 may be implemented by a different party (e.g., a third party entity 110 as shown in fig. 1A). Thus, in some embodiments, the steps performed by disease characterization module 160 to characterize a disease may additionally or alternatively be performed by a third party entity.

Referring first to resource module 140, it generates or obtains individual elements, and then constructs a resource comprised of two or more elements. The resource module 140 can store the components and/or resources in the component repository 170. Examples of elements include: 1) health aspect elements related to a disease, 2) hypothesis elements, 3) concept of interest elements defining measurable units informing health aspects of a disease, 4) measurement method elements defining how to capture raw data, 5) raw data elements specifying raw data characteristics, 6) algorithm elements for implementing algorithms for transforming raw data, 7) health data elements describing meaningful interpretation of disease related data, 8) analysis verification elements, 9) clinical verification elements, and 10) regulatory intelligence elements. Further description of these example elements is included herein.

In various embodiments, the resource module 140 may organize a single element into a resource comprised of two or more elements. As one example, the resource module 140 may organize a) health aspect elements related to a disease, B) hypothesis elements, and C) concept of interest elements into resources, hereinafter referred to as measurement definition resources. As another example, the resource module 140 may organize into resources a) measurement method elements defining how to capture raw data, B) raw data elements specifying raw data characteristics, C) algorithm elements for implementing algorithms that transform raw data, and D) healthy data elements describing meaningful interpretations of disease-related data as resources, hereinafter referred to as measurement instrument resources. As yet another example, the resource module 140 may organize a) the analytical verification element, B) the clinical verification element, and C) the regulatory intelligence element into resources, hereinafter referred to as evidence resources. Further details of these example resources are described herein.

In various embodiments, the resource module 140 generates elements and constructs resources by a de novo approach. For example, the resource module 140 identifies a particular disease, generates elements, and constructs resources that can be used to characterize the particular disease. In various embodiments, the resource module 140 may receive elements and/or resources from a third party entity (e.g., the third party entity 110 shown in fig. 1A). In such embodiments, the third party entity may be a resource developer that creates and provides its own elements and/or resources to the digital solution system 130. Thus, the resource module 140 may organize elements received from third party entities into resources. In various embodiments, the resource module 140 may organize a fusion of the de novo generated elements and the elements received from the third party entity into resources.

The target solution profile module 145 generates a Target Solution Profile (TSP) using elements and/or resources, such as elements and/or resources generated from scratch by the resource module 140 or elements and/or resources obtained by the resource module 140 from a third party entity. In various embodiments, the TSP includes measurement definition resources, measurement instrument resources, and/or evidence resources. In particular embodiments, the TSP includes each of a measurement definition resource, a measurement instrument resource, and an evidence resource. Generally, TSP represents such a measurement stack: wherein the generic description is incorporated to provide device technology agnostic profiles (e.g., profiles that are independent of specific hardware devices and independent of specific software). The generic description is valuable to ensure that resources of the TSPs can be easily interchanged. For example, the measurement instrument resources of the TSP may specify a class of devices for capturing measurement results. Examples of a class of devices include, but are not limited to: wearable devices (e.g., wrist-worn devices), wearable devices, image and voice-based devices, contactless sensors, and sensor-based smart devices (e.g., balances, thermometers, and respirators).

In various embodiments, TSP module 145 constructs TSP using a method of focusing on a disorder (e.g., a bottom-up method). Here, the TSP is constructed by first identifying the condition or disease of interest. Thus, elements of TSP are assembled for the purpose of characterizing the disease of interest. In various embodiments, TSP module 145 uses a method of focusing a measurement instrument (e.g., a top-down method) to construct TSP. Here, the TSP is built by identifying available elements and resources (e.g., elements and resources stored in element store 170). This ensures that elements and resources that have been previously generated and/or verified can be easily reused. Thus, in various embodiments, building a TSP may involve reusing elements and resources from other TSPs such that new elements and resources need not be generated. In particular embodiments, the instrumentation resources of other TSPs may be re-used to construct new TSPs, even in scenarios where other TSPs and new TSPs are developed for different diseases. The TSP module 145 may store the generated TSPs in the TSP repository 175. Further details of example TSPs are described herein.

A Digital Measurement Solution (DMS) module 150 constructs one or more DMS. In various embodiments, the DMS module 150 constructs one or more DMS by incorporating specific information into the TSP. Here, TSP represents a class of solutions for one or more DMS. For example, the DMS module 150 may incorporate specific device hardware into elements of the TSP. Thus, the DMS specifies the particular device to be used to capture the raw measurement results. As another example, the DMS module 150 may incorporate a specific algorithm into an element of the TSP. Thus, the DMS specifies a specific algorithm for transforming the raw measurement results into a meaningful health dataset, which can be interpreted as characterizing the disease.

In various embodiments, the DMS module 150 constructs two or more DMS in a common class represented by the TSP. In various embodiments, the DMS module 150 constructs three or more DMS, four or more DMS, five or more DMS, six or more DMS, seven or more DMS, eight or more DMS, nine or more DMS, ten or more DMS, eleven or more DMS, twelve or more DMS, thirteen or more DMS, fourteen or more DMS, fifteen or more DMS, sixteen or more DMS, seventeen or more DMS, eighteen or more DMS, nineteen or more DMS, twenty five or more DMS, fifty or more DMS, one hundred or more DMS, two or more DMS, three hundred or more DMS, four hundred or more DMS, five hundred or more DMS, six or more DMS, seven or more DMS, seventeen or more DMS, eighteen or more DMS, nineteen or more DMS. The DMS module 150 may store the generated DMS in the DMS repository 180. Further details of an example DMS are described herein.

The qualification protocol module 155 executes qualification protocols that enable the upgraded DMS to be quickly turned on (e.g., in view of the upgraded device and/or upgraded software version) by verifying the resulting comparability between the multiple DMS of the common class. For example, when a new device or new software package is released, the new device or new software package may be incorporated into the updated or upgraded DMS. Here, the qualification protocol module 155 implements a qualification protocol to authenticate a new DMS that incorporates a new device or new software package. This ensures that the new DMS achieves comparable results to other DMS of the same common class. Further details of this specific implementation of the qualification protocol are described herein.

In various embodiments, a DMS that has been successfully authenticated using a qualification protocol may be considered to be authenticated successfully. For example, metadata associated with a successfully authenticated DMS may be annotated. For example, the metadata may identify the qualification protocol used, as well as the fact that the DMS was successfully authenticated. In various embodiments, metadata including annotations may be accessed for inspection by a third party. Thus, a third party (such as a customer interested in using a DMS to characterize a disease) may select a DMS that has been successfully authenticated.

The disease characterization module 160 implements DMS to characterize the disease. In various embodiments, the disease characterization module 160 may be employed by a third party entity (e.g., the third party entity 110 shown in fig. 1A). For example, the third party entity may be a customer interested in characterizing the disease. Thus, a third party entity may employ the disease characterization module 160 to implement the selected DMS to characterize the disease. In various embodiments, the disease characterization module 160 may obtain measurements of interest. For example, the measurement result of interest may be raw data obtained according to a measurement method specified by the DMS. Furthermore, implementing the DMS involves transforming the measurement results of interest into meaningful health data using algorithms specified in the DMS. The disease characterization module 160 may interpret meaningful health data to characterize a disease.

The marketplace module 165 implements the marketplace of standardized solutions (e.g., DMS and TSP) and enables third party entities to access the DMS and TSP for use thereof. In various embodiments, the marketplace module 165 provides an interface to third party entities that depicts the various DMS and TSPs available for access. Such interfaces may be organized as a directory for easy access.

In various embodiments, the marketplace module 165 provides a catalog of TSPs that can be used to characterize various diseases. The marketplace module 165 may receive a selection of one of the TSPs. For example, a third party may select a TSP that characterizes a disease of interest to the third party. In addition, the third party may select the TSP because the TSP includes specifications consistent with the capabilities of the third party. In one scenario, the marketplace module 165 may provide the selected TSP to a third party. In one scenario, the marketplace module 165 may identify one or more DMS that belong to a common category represented by the selected TSP. Here, the marketplace module 165 provides one or more DMS's in a common category to third parties.

In various embodiments, the marketplace module 165 may provide suggestions to third parties that are accessing the marketplace. For example, the marketplace module 165 may provide suggestions to third parties identifying one or more elements, one or more resources, one or more TSPs, or one or more DMS. This may be useful to third parties who may need additional guidance regarding the best standardized solution that will meet their needs.

In various embodiments, the marketplace module 165 receives suggestions regarding additional standardized solutions that may be valuable. For example, the marketplace module 165 may receive suggestions from a third party for particular DMS or TSPs that are not present in the marketplace. Such third parties may be resource developers or customers who find gaps that are not met by the current products of standardized solutions. For example, the recommendation may identify that the specification of a particular device exceeds the specification of the available TSPs and DMS. Accordingly, the marketplace module 165 may provide suggestions to any of the resource module 140, the TSP module 145, and/or the DMS module 150 to generate additional standardized solutions that may be included in the marketplace.

In various embodiments, the marketplace module 165 provides a catalog of target solution profiles and receives search queries. For example, a third party presenting a catalog of target solution profiles may provide a search query for a particular element or resource in the target solution profiles. In various embodiments, a third party provides a search query for concepts of interest or for a particular disease. The marketplace module 165 queries available TSPs (e.g., TSPs stored in the target solution profile repository 175) from the search query and then returns a list of TSPs that satisfy the search query. For example, if the search query specifies a particular concept of interest, the marketplace module 165 evaluates elements of the TSP for concepts of interest that satisfy the search query. Accordingly, the marketplace module 165 may provide (e.g., to a third party) a list of TSPs that satisfy the search query.

Example measurement Stack

Embodiments disclosed herein relate to constructing TSPs and DMS, and implementing TSPs and DMS to characterize a disease. Generally, TSPs and DMS are built on a measurement stack consisting of one or more elements (also referred to herein as "layers"). That is, the measurement stack provides a structure or framework for the TSP or DMS. The elements and/or resources of the measurement stack may be generated and/or maintained by the resource module 140 as described above with reference to fig. 1B.

The purpose of the measurement stack is to fill the aforementioned gap, e.g. lack of standardization and concerns about data collection, analysis and interpretation. First, the measurement stack provides a standardized structure that represents a general way to describe the solution, allowing standardization. Second, the measurement stack initiates and allows for coordinated coordination between multiple resources and elements. Third, the measurement stack model will enable resources to be converted between diseases and use cases, thereby enabling reusability of the component hierarchy.

In various embodiments, the measurement stack includes one or more resources. Examples of resources include measurement definition resources, measurement instrument resources, or evidence resources. A resource refers to one or more elements of a stack. In various embodiments, a resource refers to two or more elements. In various embodiments, a resource refers to three or more elements. In various embodiments, a resource refers to three or more elements.

In various embodiments, the measurement definition resource includes two elements. In various embodiments, the measurement definition resource includes three elements. In various embodiments, the measurement definition resource includes four elements. In various embodiments, the measurement instrument resource includes two elements. In various embodiments, the measurement instrument resource includes three elements. In various embodiments, the measurement instrument resource includes four elements. In various embodiments, the evidence resource includes two elements. In various embodiments, the evidence resource includes three elements. In various embodiments, the evidence resource includes four elements.

In various embodiments, the measurement stack includes two resources. For example, a measurement stack may include measurement definition resources associated with a particular disease, as well as measurement instrument resources describing the capture of data that may be used to characterize the disease. In a specific embodiment, the measurement stack includes three resources. For example, a measurement stack may include measurement definition resources associated with a particular disease, measurement instrument resources describing the capture of data available to characterize the disease, and evidence resources for validating a meaningful data set for the disease.

In various embodiments, elements of the resource are connected to each other. For example, an element of a resource is configured to communicate with at least another element of the same resource. For example, within a resource, the elements are organized into layers, and thus, a first element is configured to communicate with a second element that is adjacent to the first element. This enables information to be transferred from one element to the next.

In various embodiments, elements of a first resource are connected to elements of a second resource. Thus, an element of a first resource may communicate with an element of a second resource. As an example, the first resource may be located lower in the measurement stack relative to the second resource within the measurement stack. Here, the elements of the first resource may be connected to the elements of the second resource, thereby enabling the first resource and the second resource to interface with each other.

In various embodiments, the resources of the measurement stack are ordered (from bottom to top of the stack) as follows: 1) Measurement definition resources and 2) measurement instrument resources. In a specific embodiment, the resources of the measurement stack are ordered (from bottom to top of the stack) as follows: 1) Measurement definition resources, 2) measurement instrument resources, and evidence resources.

Referring now to FIG. 2A, an example measurement stack is shown, according to one embodiment. As shown in fig. 2A, an example measurement stack includes: a particular disease (referred to as a "disorder" in fig. 2A); measurement definition resources (labeled "definition" in fig. 2A) that include meaningful health aspects (MAH); a measurement instrument resource (labeled "measurement instrument" in fig. 2A) comprising elements related to capturing data, algorithms, and datasets; and describes one or more validated evidence resources (labeled "evidence" in fig. 2A) for validating the generated dataset. Exemplary conditions are further detailed in table 1. Exemplary meaningful health aspects (MAHs) are detailed in table 2.

As shown in fig. 2A, the specific disease is located at the bottom of the measurement stack. Here, the disease may control the generation or selection of one or more of the above-mentioned resources in the measurement stack. For example, the disease control measure defines the generation or selection of resources.

Generally, a measurement definition resource defines a measurable concept associated with the disease. Thus, the measurable concepts provide information that is used to characterize a disease (e.g., the presence of a disease, the severity of a disease, the progression of a disease, etc.). For example, for atopic dermatitis conditions, the measurement definition resource may define the concept related to atopic dermatitis as nocturnal scratching. Thus, the subject's nocturnal scratching may be measured to characterize the subject's disease (e.g., a higher amount of nocturnal scratching may indicate more severe atopic dermatitis than a lower amount of nocturnal scratching). Examples of measuring individual elements defining a resource are described in further detail herein.

The measurement instrument resource defines how to capture measurement concepts related to the disease and further how to transform the captured raw data into an interpretable, meaningful health dataset. For example, the measurement instrument resource may describe a device specification that affects raw data capture. Furthermore, the measurement instrument resource may transform raw data into a health dataset that is more meaningful for a particular disease. The meaningful health dataset may be a measurement of the concept related to the disease (as described in relation to measurement definition resources) or may be easily interpreted to obtain a measurement of the concept related to the disease. Returning to the atopic dermatitis example described above, a meaningful health data set may include a measure of nocturnal scratching (e.g., scratching event per hour, scratching duration per hour, total scratching event). Alternatively, the meaningful health data set may be a data set from which a measure of nocturnal scratching (e.g., scratching event per hour, scratching duration per hour, total number of scratching events) can be easily extracted. Examples of the various elements of the measurement instrument resource are described in further detail herein.

The evidence resource includes one or more validations that validate the data set generated by the measurement instrument resource. This ensures that the data set (e.g., health data set) generated by the measurement instrument resource is accurate and thus can be used to accurately characterize the disease. Examples of verification included in the evidence resource may include technical verification, analytical verification, and/or clinical verification. In various embodiments, the evidence resource includes two or more verifications. In various embodiments, the evidence resource includes three or more verifications. Generally, performing verification of evidence resources ensures that the measurement results are accurate and thus may be considered to meet (e.g., as a standard for) clinical trial use and approval. Examples of the various elements of the evidence resource are described in further detail herein.

In various embodiments, different resources of the measurement stack are specifically selected or generated for a particular disorder. For example, a measurement definition resource may describe concepts that are particularly relevant to the condition, and thus, the measurement definition resource may be specific to the condition. In some embodiments, the different resources in the measurement stack are interchangeable and thus may be used in measurement stacks for various diseases. For example, the meter resources may be interchangeable such that the meter resources may be included in a first measurement stack for a first condition and may be further included in a second measurement stack for a second condition. In this way, interchangeable or reusable resources enable more efficient generation and construction of measurement stacks.

FIG. 2B is an example measurement stack showing various elements, according to one embodiment. As shown in fig. 2B, the measurement stack includes three resources (e.g., a measurement definition resource, a measurement instrument resource, and an evidence resource). Each of these three resources is made up of two or more elements. Specifically, the measurement definition resources include: 1) health aspect elements, 2) hypothesis elements, and 3) concept elements of interest. The measuring instrument resources include: 1) a measurement method element, 2) a raw data element, 3) an algorithm element, and 4) a health data element. Evidence resources include: 1) An analytical verification element, and 2) a clinical verification element. As shown in fig. 2B, there are a total of 8 elements in the measurement stack.

In various embodiments, the measurement stack may be arranged differently so that additional elements or fewer elements are included. In various embodiments, although not shown, the measurement stack may further include a supervision element, which is valuable for comparing a supervision expert to a measurement endpoint. Such a supervision element may be included in an evidence resource. Thus, in such an implementation, there are a total of 9 elements in the measurement stack.

In various embodiments, the functionality of two or more elements in a resource may be combined into a single element. Thus, there may be fewer than the 8 elements explicitly shown in fig. 2A in the measurement stack. For example, assume that an element and a conceptual element of interest may be combined into a single element. As another example, the health aspect element, the hypothesis element, and the concept of interest element may be combined into a single element.

Reference is first made to a disorder (e.g., a physical disorder or a medical disorder as shown in fig. 2B), which may refer to any disease. Examples of conditions or diseases include atopic dermatitis, parkinson's disease, alzheimer's disease, chronic Obstructive Pulmonary Disease (COPD), pulmonary Arterial Hypertension (PAH), asthma, retinal disease, major Depressive Disorder (MDD) or cancer. Additional examples of disorders are described herein and shown in table 1.

The meaningful health aspect (MAH), referred to as health aspect in fig. 2B, generally defines the aspect of the disease to be ameliorated. Examples of meaningful health aspects (MAH) are shown in table 2. The assumption is that this means to improve this meaningful health aspect. For example, a hypothesis may relate to an intervention means (e.g., a therapeutic intervention, a surgical intervention, or a change in lifestyle) predicted to improve the meaningful health aspect associated with the disease. Generally, improving meaningful health aspects is associated with improving disease. In various embodiments, there may be a number of meaningful health aspects available for a particular disease (e.g., aspects of the disease to be ameliorated).

The concept of interest describes a measurable unit that informs of meaningful health aspects. For example, the concept of interest is a measurable unit that can be used to inform meaningful health aspects related to disease, and thus, the severity of the disease. Examples of concepts of interest (COI) are shown in table 3. In various embodiments, the concept of interest describes medical measurements of a disease (e.g., a measurable unit that the health care community will measure to determine the severity of the disease). For example, in the context of parkinson's disease, the medical measurement of parkinson's disease is tremor. Here, the amount of tremor may be a measure of the severity of the disease. In various embodiments, the concept of interest describes a measurable experience of an individual suffering from the disease. Here, the measurable experience may not be a medically relevant unit of measure, but may still have a significant impact on the patient afflicted with the disease. In such embodiments, the concept of interest may be a symptom of a disease that the patient wants to alter. For example, also in the context of parkinson's disease, a measurable experience of an individual suffering from parkinson's disease may be sleep deprivation. Although sleep deprivation is not a medical measurement unit for parkinson's disease, it is still a measure that can provide information on the severity of the disease.

Reference is next made to measurement method elements which generally describe a solution implemented for capturing data of concepts of interest. Solutions to the measurement method elements include, for example, hardware, software, or firmware solutions. Example solutions for measuring method elements include sensors, devices such as computing devices, cellular devices, or wearable devices, and mobile applications. In various embodiments, the sensor may be built into a device (such as a wearable device or a cellular device).

In various embodiments, the measurement method element identifies a specification of the measurement method. For example, for a wearable device, a measurement method element identifies an operating specification of the wearable device (e.g., a frequency or frequency range (e.g., 10Hz to 100 Hz) at which the device captures data, a time interval at which the device captures data (e.g., 24 hours a day, or in response to a command), a presence of one or more sensors of the wearable device capturing data, a storage capacity of the wearable device, and/or an estimated battery life). In various embodiments, the measurement method employs a product that uses algorithms to process data captured by (mobile) sensors to generate measures of behavioral and/or physiological functions. This includes novel feature metrics and indicators for which the underlying biological process is not yet understood. As with other digital pharmaceutical products, these products may be characterized by a large body of evidence supporting the quality, safety, and effectiveness indicated in their performance requirements.

Referring next to the raw data, this element represents the raw data set captured according to the particular method of the measurement method element. For example, if the measurement method element identifies a wearable device (and corresponding specifications), the raw data represents a dataset captured by the wearable device according to the specifications. In various embodiments, the raw data itself does not provide interpretable meaningful data. As a specific example, the raw file may include data captured at an acceleration of 10Hz to 100 Hz. This data was captured in 3D SI units (XYZ gravity) and 28 days of data was collected continuously. Briefly, this example describes what raw data (acceleration in 3D SI units), its frequency (10 Hz to 100 Hz), and the amount of data captured (28 days).

Reference is next made to an algorithm that transforms raw data from raw data elements into meaningful data sets (e.g., meaningful health data related to measuring concepts of interest). Returning again to the atopic dermatitis example, the algorithm interprets the raw measurement device data captured during sleep and transforms the raw data into meaningful health data (e.g., a scratch event). In some scenarios, the algorithm is specific to a particular measurement method. Thus, a particular algorithm in the algorithm element can only convert raw dataset results captured from a particular measurement method.

Reference is next made to a health data element, which includes health data, also referred to herein as meaningful health data or meaningful health data sets. Health data is transformed from raw data by algorithms, representing interpretable datasets that provide information for specific concepts of interest. Returning again to the atopic dermatitis example, the health data may include or be readily interpreted to include any of total sleep time, scratching events per hour, and total number of scratching events. Here, health data is the result of an algorithm/other process that converts "raw data" into its final health-related data. One example may include converting accelerometer data into a number of steps. This process may have intermediate stages, for example, identifying each severe symptom onset during the day may be a step, and then further refinement is to calculate the average time of all of these. Both of which may be classified as health data.

Reference is next made to an analytical verification element, which involves verifying one or more other elements in the measurement stack. In various embodiments, the input of the analytical verification includes measuring elements defining the resource and measuring elements of the instrument resource. The output of the analytical verification includes supporting evidence of success or failure of the corresponding solution verification incorporating the elements of the measurement definition resource and the elements of the measurement instrument resource. Generally, digital measurement solutions are incomplete unless the results they produce prove analytically effective to support clinical interpretation. During analytical validation, the digital measurement solution is exposed to a series of test conditions and program pressures to generate sample data, and the results are recorded for statistical analysis. These results verify or redefine functional ranges beyond which the reliability of the measurement results may be problematic. Successful analytical verification will mean that a solution conforming to the profile can support accurate markup declaration without unexpected risks and consequences.

In various embodiments, the analytical verification element may perform analytical verification of device specifications, algorithms, and health data output. In various embodiments, the analytical verification involves comparing the data to appropriate measurement criteria. Example measurement criteria for various diseases may be established by a third party, or in a community. For example, an example measurement standard for different diseases may be the standard established by ICHOM Conect.

For example, analytical verification ensures that meaningful health data meets the necessary sensitivity requirements, specificity requirements, and/or reliability requirements. In various embodiments, the requisite sensitivity requirement is any of at least 50% sensitivity, at least 60% sensitivity, at least 70% sensitivity, at least 75% sensitivity, at least 80% sensitivity, at least 85% sensitivity, at least 90% sensitivity, at least 91% sensitivity, at least 92% sensitivity, at least 93% sensitivity, at least 94% sensitivity, at least 95% sensitivity, at least 96% sensitivity, at least 97% sensitivity, at least 98% sensitivity, or at least 99% sensitivity. In various embodiments, the requisite specificity requirement is any of at least 50% specificity, at least 60% specificity, at least 70% specificity, at least 75% specificity, at least 80% specificity, at least 85% specificity, at least 90% specificity, at least 91% specificity, at least 92% specificity, at least 93% specificity, at least 94% specificity, at least 95% specificity, at least 96% specificity, at least 97% specificity, at least 98% specificity, or at least 99% specificity. In various embodiments, the necessary reliability requirement is any of at least 50% reliability, at least 60% reliability, at least 70% reliability, at least 75% reliability, at least 80% reliability, at least 85% reliability, at least 90% reliability, at least 91% reliability, at least 92% reliability, at least 93% reliability, at least 94% reliability, at least 95% reliability, at least 96% reliability, at least 97% reliability, at least 98% reliability, or at least 99% reliability.

In various embodiments, analytical verification enables comparison of digital solutions provided by a measurement stack with reference metrics currently employed or previously developed to characterize a disease. For example, returning to the atopic dermatitis example, analytical verification may determine that the digital solution provided by the measurement stack is capable of properly measuring nocturnal scratching, according to appropriate reliability requirements, specificity requirements, and sensitivity requirements. Here, the digital solution may be comparable to or better than the reference metric (e.g., infrared observations to monitor nocturnal scratching).

In various embodiments, the analytical verification element comprises analytical verification, in addition to or alternatively to technical verification. In various embodiments, technical validation verifies that the data set (e.g., raw data from the raw data element and health data from the health data element) is appropriate. As one example, the technical verification may evaluate whether the captured raw data conforms to a firmware protocol and/or a software protocol specific to the device. As another example, the technical verification may evaluate the location of raw data captured by the measurement method according to a specification identified in the measurement method. For example, these specifications may include battery life, data storage space, available measurement frequencies. Thus, technical verification determines whether raw data captured by the measurement method meets these specifications. As one example, if the raw dataset indicates that the data was captured at a frequency that exceeds the specifications identified in the measurement method, then the technical verification may flag the problem and the verification process fails. Alternatively, if the original dataset indicates that the data was captured at a frequency within the specifications identified in the measurement method, the technical verification may be deemed successful. Additional details and examples of technical validation are described in Goldsack, J.C. et al, verifications, analytical validation, and clinical validation (V3): the foundation ofdetermining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs), npj digit. Med.3,55 (2020), which is hereby incorporated by reference in its entirety.

Reference is next made to a clinical verification element, which involves clinical verification of a digital solution. Clinical validation is a process of evaluating whether a measurement solution acceptably identifies, measures, or predicts a meaningful clinical, biological, physical, functional state, or empirical course in a given use environment. Knowing what level of accuracy, precision and reliability is valuable for solutions useful in a particular clinical research setting. Clinical validation aims at taking measurements that have undergone verification and analytical validation steps and evaluating whether they are able to answer a specific clinical question. Generally, digital measurement solutions are incomplete unless the results they produce are interpretable from a clinical perspective and are sufficiently relevant to meaningful health aspects of the disease. Here, the clinical verification element provides guidelines for clinically interpreting the measurement results.

For example, clinical validation may involve analyzing whether a digital solution identifies, measures, and predicts a meaningful clinical, biological, physical, functional state, or experience associated with a disease.

As one example of clinical validation, guidelines identifying that temperature measurements in increments of 0.00001% are not relevant to clinical decision making may be included. Further, a temperature standard of 0.1 degree increment may be identified. In various embodiments, the clinical validation is in vivo validation performed in a specific target population. Thus, clinical validation means checking whether the measurement stack is able to answer clinical questions related to the disease effectively. Returning again to this atopic dermatitis example, clinical validation may involve evaluating the effect of treatment of nocturnal scratching in the intervening patient population. Here, the intervention is expected to reduce the amount of nocturnal scratching. The specific procedure identified in the clinical trial may be performed during or after the clinical trial to ensure that changes in the patient population are accurately assessed, which provides an accurate assessment of the impact of the intervention. Further details of clinical validation are described in Goldsack, J.C. et al, verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs), npjDigit. Med.3,55 (2020), which is hereby incorporated by reference in its entirety.

In various embodiments, the measurement stack further comprises a supervision element for comparing a supervision expert with the measurement endpoint. Generally, the regulatory element may include regulatory guidelines. In various embodiments, the regulatory element includes scientific advice from a third party (such as a third party supervisor). For example, for the "nocturnal scratching" measurement, it is necessary to normalize what is referred to as "nocturnal". Thus, the regulatory element may identify guidelines for defining "nighttime" (e.g., the start time when someone tries to fall asleep), an example of which may be a measure defined as the percentage of time a patient is conscious in their Total Sleep Opportunity (TSO) time. Additional examples of regulatory guidelines may be standard guidelines (e.g., guidelines issued by the U.S. Food and Drug Administration (FDA), such as the FDA patient report results (PRO) guidelines or the FDA patient-centric drug development (PFDD) guidelines series).

In various embodiments, the regulatory element includes guidelines that facilitate the implementation of regulatory acceptance. For example, different regulatory approaches involve different requirements for achieving regulatory acceptance. In some scenarios, the request may be submitted through an FDA CPIM conference, within IND, or through a formal qualification procedure. In particular embodiments, the supervising element evaluates one or more other elements of the measurement stack, such as elements of the measurement definition resource and/or other elements of the evidence resource. Thus, the regulatory element can save resources by having the supervisor participate as early as the use environment (COU), digital metrics (medical devices, digital biomarkers, clinical outcome assessment) and all verifications (e.g., technical, analytical, and clinical) can obtain approval.

In various embodiments, the regulatory element may be available to third parties (such as regulators) that may further cooperate to co-develop and/or propose improvements to standardized solutions. This enables dynamic regulatory evaluation of standardized solutions. For example, the supervisor may provide new evidence requests and questions in more real-time. In response, new evidence, comments, and additional context may be provided to the supervisor. In various embodiments, dynamic regulatory assessment involves multiple stakeholders (e.g., involving customers, resource developers, pharmaceutical companies, regulators, etc.), and thus, regulatory elements may be used by multiple stakeholders to enable collaborative methods to enable regulatory approval of standardized solutions. An example of dynamic regulatory assessment is described below in example 5.

In various embodiments, the supervisor may evaluate the tolerance and/or deviation of the standardized solutions (e.g., DMS). Here, the supervision element may provide guidelines for knowing the magnitude of the intended therapeutic effect. If the effect is large, the tolerance may be large; if the effect is small, the measurement also needs to be more accurate. In various embodiments, the regulatory element may relate to regulatory advice (e.g., which metrics are meaningful) that is given independent of the intervention means.

Fig. 2C is an example measurement stack indicating one or more elements forming a Target Solution Profile (TSP), a Target Instrumentation Profile (TIP), or a target element profile (TCP), according to one embodiment. In various embodiments, the TSP encompasses the complete measurement stack (e.g., all 9 layers as shown in fig. 2C). In contrast, TIP and TCP include fewer than all of the elements of the measurement stack. For example, TIP comprises six elements (from concept of interest until technical/analytical verification). TCP refers to the individual elements of the measurement stack.

In general, TSPs are considered solution agnostic (e.g., no specific brands and versions are specified). TIP is instrument-centric and measurement defines certain elements of the resource (e.g., symptoms, meaningful health aspects, assumptions) as well as certain elements of the evidence resource (e.g., clinical validation and regulatory intelligence) are agnostic. In addition, TIP is considered disorder agnostic because no element of the TIP layer is associated with a particular disorder, meaningful health aspects, and patient population. This adds new value to the available resources provided by stakeholders and conforms to these TIPs. For example, TIP may be interchanged between different TSPs designed for a particular disorder. Thus, a developer (e.g., a developer of individual elements or resources) may develop functional resources that cover a variety of disorders. This is in contrast to having a developer develop new resources and study designs for each particular condition. Newly developing separate resources may be wasteful of material resources, as the required resources may often already be available as ready solutions. In addition, the resources included in the TIP can be easily reused for a variety of conditions. By conducting clinical verification for a particular disorder, TIP can be used for a variety of disorders, allowing for improved reusability and sustainability of available resources. For example, a class of body movement recorder solutions for measuring Daily Living Physical Activity (DLPA) of a first disorder of pulmonary arterial hypertension was validated. Analytical verification has verified the ability to measure DLPA parameters by devices included in the TIP. Furthermore, DLPA may also be a concept of interest in Parkinson's Disease (PD) secondary disorders. Thus, the validated TIP for measuring DLPA can be reused for the second condition of PD without having to re-perform the technical and analytical validation steps, as these are already included in the TIP. In addition, the TIP provides a structure to the available resources to prevent stakeholders from being inundated with countless TSPs, devices and algorithms that are available for digital metrics. Thus, solutions within one TIP can be easily compared to deliver the most desirable solution for a new study design. This increases the likelihood of choosing the best measuring instrument for a particular use case.

As shown in fig. 2C, TCP defines a single generic element. Thus, TCP includes a general description of the technical details of the various elements. For example, the TCP identified in fig. 2C may include a general description of the measurement method (e.g., a three-axis accelerometer (wrist-worn), 10Hz to 100Hz, 18+hour battery life, 500+mb data storage space). In general, TCP allows for easy replacement of resources of the same or similar TCP into multiple TIPs and TSPs. For example, TSP includes a measurement method (Apple Watch 6) with a battery life of 18+hours, but a specific study design requires a battery life of at least 96 hours. Thus, the 18+hour-describing TCP may be replaced with a different TCP having a battery life of at least 96 hours. This precludes the availability of Apple Watch 6 in TIP/TSP and will include only the required elements. TCP thus allows researchers to more easily and optimally meet their particular needs.

Example target solution Profile

As described herein, TSP encompasses a complete measurement stack. The generation and maintenance of TSPs may be performed by TSP module 145 as described above in fig. 1B. The various elements of the TSP are described using a generic description (in contrast to DMS which describes a specific solution) to provide a device technology agnostic profile. Device technology agnostic refers to not only hardware agnostic (e.g., agnostic to a particular device), but also software agnostic (e.g., software version agnostic, such as software for an application or software for a device). This prevents any association with a particular brand, model or technology, and allows a particular DMS to be smoothly modeled as a generic TSP. Thus, a TSP is a generic profile defining a class of DMS, where each DMS provides further specificity to the generic profile. Since TSPs are device technology agnostic, it is easier to provide novel solutions for the same or even different conditions. This makes it possible to improve the development of sustainable solutions for the future. Furthermore, TSPs provide coordination in a ecosystem and show a high potential to shorten the lifecycle of solution development, as early generated evidence can be re-used. Taken together, TSP shows the potential to ultimately accelerate the adoption of digital metrics in clinical studies. Example TSPs are further detailed in table 4.

The TSP provides a generic description covering multiple DMS within the same solution class. DMS conforming to the class represented by the TSP may be considered to conform to the purpose of the same use case. Since the TSP is device technology agnostic, the contained DMS shows improved reusability of resources. TSPs are no longer used for only one study, but rather accelerate the reuse of available resources and increase the value of all resources included in the DMS. Also, the TSP categories may be utilized to compare subtle differences between similar TSPs. This allows stakeholders to more easily compare multiple TSPs (and DMS in the class represented by the TSPs) to select the best preference (e.g., cost, equipment weight, or battery life) for their particular use cases. In various embodiments, the DMS may also conform to a general description of various TSPs.

Furthermore, TSPs allow version management (lifecycle management of digital measurement solutions). Over time, available devices, algorithms, and techniques continue to evolve. In various embodiments, the TSP may be edited and modified to obtain an updated version (which may coexist with an older version). This allows for upgrades to elements of the solution if the solution as a whole still meets the TSP criteria. Verification of the evolved TSP is evaluated by a Qualification Protocol (QP). QP validates version management and ensures that TSPs are deemed to accommodate future requirements. In various embodiments, QP allows version management of specific resources and can verify comparability between multiple DMS. Qualification protocols are further described.

FIG. 2D illustrates an example target solution profile according to one embodiment. Here, the elements of TSP are described in general terms. However, while the TSP shown in fig. 2D identifies a general disease or condition, in various embodiments, the TSP identifies a specific disease or condition.

See measurement method elements, which describe a device-agnostic measurement method. For example, a measurement method element may specify a particular class of devices. Examples of a class of devices may include, but are not limited to: any of wearable devices, image and voice based devices, contactless sensors, and sensor based smart devices (e.g., balances, thermometers, and respirators). The measurement method elements may further comprise specifications of the measurement method, such as battery life, data storage space, available measurement frequencies. Thus, the developer may determine whether the TSP is appropriate for its digital solution based on the specifications of the measurement method (e.g., if the developer needs to capture data for at least 96 hours, but the TSP measurement method only specifies a battery life of 18 hours, then the developer determines that a different TSP is needed).

See raw data elements of TSP, which describe raw files captured according to the measurement method. For example raw data according to the measurement method specifications. Thus, if the measurement method indicates a measurement frequency of 100Hz, the raw data element describes a raw file that includes data captured at the measurement frequency of 100 Hz. In various embodiments, the digital measurements reported by the measurement methods are derived through a data supply chain that includes hardware components, firmware components, and software components. The term "raw data" is used to describe data that exists in an early stage of the data supply chain. The sensor output data for the sample level (e.g., 50Hz accelerometer signal or 250Hz ECG signal) will be raw data. Although signal processing methods may have been applied to the data (e.g., downsampling, filtering, interpolation, smoothing, etc.), the data is still considered "raw" because it is a direct representation of the raw signal produced by the sensor.

See the algorithmic elements of the TSP, which identify one or more algorithms that may suitably transform the raw data into meaningful health data. Here, the algorithm is designed according to a specific measurement method for capturing raw data. As an example, if the measurement method indicates a measurement frequency of 100Hz, the algorithm is designed to transform data specifically captured at the frequency of Hz. In various embodiments, the algorithmic elements represent a series of data manipulation processes embedded in firmware and software, including but not limited to signal processing, data compression and decompression, artificial intelligence, and machine learning. An algorithm is a calculation that transforms data from a sensor into meaningful information. The algorithm may be directly part of the sensor or may be operated by a party to perform additional data science operations to create the derived metric.

See analytical verification elements that enable verification of elements of the measurement instrument resource (e.g., measurement methods, raw data, algorithms, and health data) to ensure that the raw data and/or health data reliably, efficiently, and sensitively meet appropriate criteria. In various embodiments, analytical verification occurs at the intersection of engineering expertise and clinical expertise. Analytical verification involves evaluating the processed data and requires testing of human subjects. After the verified sample level data has been generated by the measurement method, algorithms are applied to these data to create behavioural or physiologically significant indicators. The process begins at the point where the verified output data (sample level data) becomes the data input for algorithmic processing. Thus, the first step of analytical verification requires a defined data capture protocol and a specified population of test subjects. During the analytical verification process, the index generated by the algorithm is evaluated against an appropriate reference standard.

In various embodiments, TSP may be constructed using a method of focusing on a disorder (e.g., a bottom-up method). In various embodiments, TSP may be constructed using a method of focusing a measurement instrument (e.g., a top-down method). Regardless of the method (e.g., bottom-up or top-down), the final TSP and DMS of this class may be the same.

Referring first to the method of focusing on a condition, a specific condition is identified. Here, a measurement definition is determined that is meaningful to the patient suffering from the disorder. This includes determining concepts of interest to be measured. Next, suitable measuring instruments are developed, or if available, off-the-shelf solutions may be selected. For example, the measurement instrument resources of a different TSP may be selected and reused for the current TSP. Assuming that the meter resources of the TSP are generally described in general terms, the reuse of the meter resources for the current TSP may require little or no additional effort. Next, evidence resources are generated for the TSP. In various embodiments, generating evidence resources involves determining techniques and analytical verification of a measurement instrument suitable for the TSP. In various embodiments, elements of the evidence resource (such as elements for performing technical and analytical verification) may be reused from another TSP. Assuming that technical and analytical verification may have been previously performed on a common instrumentation resource of another TSP, the current TSP need not re-perform the same technical and analytical verification again. In various embodiments, the component of the evidence resource comprises a clinical validation component. Here, the clinical verification element verifies whether the result is clinically valid. Thus, generating evidence resources includes generating clinical validation elements that are significant to ensure that the results of TSPs are of clinical value.

See methods of focusing the measuring instrument, which begin with finding available elements. Elements with similar measuring instruments are identified and then divided into several classes of solutions. These individual elements are divided into individual element classes called TCP (only one layer of the measurement stack is generally described, e.g. measurement methods or algorithms). If the concept of interest, measurement method, raw data, algorithms, health data, and technical/analytical verification are employed, multiple TCPs at different layers may be consolidated into a TIP. TSPs are built on top of these TIPs with consistent definition and validated instrumentation by making them complete with a disease-related layer. TSPs are constructed, for example, by generating meaningful health-aspect elements and clinical validation elements on top of these elements of the TIP.

The method of focusing the measuring instrument eases the development of TCP, multiple TIPs and many TSPs. In the approach of focusing the measuring instrument, smaller stakeholders can more easily contribute to resource development, as they typically have resources as measuring methods and algorithms within their digital portfolio. Resource providers can now pay more attention to the development of one element, which may be useful for a variety of studies.

In various embodiments, given a complete TSP, the resources and elements of the TSP may be quickly formulated. For example, TIP and TCP may be quickly formulated from the full TSP by excluding definition related layers or by selecting a separate layer, respectively.

Resources and elements of the TSP may be interchangeable or may be replaced as described herein. For example, a TIP from a first TSP may replace a second TIP in a second TSP. Here, replacing the TIP may be beneficial because the verification required to account for the replacement is minimized. For example, only the technical/analytical verification of TSPs (including now replaced TSPs) is re-evaluated. As another example, only clinical validation of TSPs (including now replaced TSPs) is re-evaluated (e.g., no technical/analytical validation needs to be performed).

Example digital measurement solution

Fig. 2E shows an example digital measurement solution according to the first embodiment. Fig. 2F shows an example digital measurement solution according to a second embodiment. Although not explicitly identified in fig. 2E and 2F, each DMS may be developed for a specific disease or disorder. As one example, the DMS in fig. 2E and 2F may be developed to characterize pulmonary arterial hypertension. The generation and maintenance of the DMS may be performed by the DMS module 150, as described above in fig. 1B.

Referring first to the DMS shown in fig. 2E (referred to as DMS # 1), which is not device technology agnostic, as is the case with the TSP shown in fig. 2D. Instead, DMS #1 explicitly identifies the particular device used to capture the raw data. Here, the measurement method element of DMS#1 identifies the Actigraph GT9X Link device that captured the raw data. The Actigraph GT9X Link device includes specific specifications including presence of gyroscopes, accelerometers, thermometers and magnetometers, measurement frequency of 100Hz, storage space of 4GB, and battery life of 1 day. These specifications of the Actigraph GT9X Link device can satisfy the specifications of the measurement method of the corresponding TSP indicating the class of which the DMS #1 is a part. Although not explicitly shown, the measurement method elements of DMS #1 may further specify software for capturing raw data. For example, the measurement method element of DMS #1 may specify the software version (e.g., operating system version) on which the Actigraph GT9X Link device is running.

The algorithm element of DMS #1 identifies a specific algorithm (e.g., actiGraph deterministic algorithm, such as actionlife 6) that can transform raw data captured by ActiGraph GT9X Link devices into meaningful healthy data sets. Also, in contrast to the corresponding TSPs of the general description algorithm, the algorithm elements of DMS#1 identify a specific algorithm that can be executed.

Referring next to the DMS shown in fig. 2F (referred to as DMS # 2), the DMS is no longer device technology agnostic, as is the case with TSPs. Here, DMS #2 explicitly identifies the Garmin Vivofit 4 device in the measurement method element. Thus, the Garmin Vivofit 4 device may be used to capture raw data according to the specifications of the device. The measurement method elements identify those specifications, including the presence of gyroscopes, accelerometers, thermometers and magnetometers, measurement frequency of 50Hz, storage space of 2GB, battery life of 7 days. The algorithm element of DMS #2 identifies a specific algorithm (e.g., a machine learning algorithm) that transforms the raw data captured by the Garmin Vivofit 4 device into a meaningful health dataset. Also, in contrast to the corresponding TSPs of the general description algorithm, the algorithm elements of DMS#1 identify a specific algorithm that can be executed. Although not explicitly shown, the measurement method elements of DMS #2 may further specify software for capturing raw data. For example, the measurement method element of DMS #2 may specify the software version (e.g., operating system version) on which the Garmin Vivofit 4 device is running.

Here, the DMS shown in fig. 2E and 2F (e.g., DMS #1 and DMS # 2) may belong to a common category represented by the target solution profile. The device specifications of the measurement method elements of DMS #1 and DMS #2 may satisfy the specifications identified in the measurement method elements of the target solution profile. For example, the measurement method element of the target solution profile may identify one or more of the following device specifications: 1) availability of gyroscopes, accelerometers, thermometers and magnetometers, 2) measurement frequency between 1Hz and 100Hz, 3) storage space between 1GB and 4GB, and 4) battery life between 1 day and 7 days.

In various embodiments, the elements of DMS #1 and DMS #2 may be interchangeable. For example, the measurement method element of DMS#1 can replace the measurement method element of DMS#2. As another example, the algorithm element of DMS#1 can replace the algorithm element of DMS#2. In various embodiments, a set of elements of DMS #1 and DMS #2 may be interchangeable. For example, the measurement method elements and algorithm elements of DMS #1 may replace the measurement method elements and algorithm elements of DMS #2, respectively. As another example, the measurement instrument resources (e.g., measurement method elements, raw data elements, algorithm elements, and health data elements) of DMS #1 may replace the corresponding measurement instrument resources of DMS # 2. As another example, the target measurement instrument configuration file of DMS #1 (e.g., concept of interest, measurement method element, raw data element, algorithm element, health data element, and analytical verification element) may replace the corresponding target measurement instrument configuration file of DMS # 2.

Other examples of Digital Measurement Solutions (DMS) are further detailed in table 5.

Lifecycle management for TSPs and DMSs

Digital measurement solutions are subject to a rapidly evolving lifecycle, since elements of the measurement instrument resource are always faced with the possibility of upgrades (e.g. due to new device releases or new software releases). Maintaining equivalence between different measurement solutions and versions while managing rapid technology evolution is a new challenge. For example, these upgrades may be bug fixes or adding novel features to the available devices.

In various embodiments, qualification protocols are implemented to improve lifecycle management of fast evolving elements relative to their TSPs. The enforcing qualification protocol may be performed by the qualification protocol module 155 (see FIG. 1B). Generally, the Qualification Protocol (QP) validates the upgraded functionality without re-evaluating the complete digital measurement solution or the complete target solution profile. For example, QP may be implemented to ensure that a digital measurement solution incorporating an upgraded version (due to new device release or new software release) implements a comparable solution as compared to the previous version.

In various embodiments, the QP evaluates the new TSP and/or the new DMS based on the upgraded device or software release, and upon successful verification, the new TSP or DMS may be stored as part of, for example, a marketplace or catalog. Here, the new TSP or DMS may replace the previous solution (e.g., the previous TSP or the previous DMS). In various embodiments, this may involve replacing hardware elements and/or delivering software upgrades. In various embodiments, a new TSP or DMS that has been validated using QP may represent additional resources, for example, for inclusion in a marketplace or catalog. For example, the new DMS may include upgraded equipment or upgraded software and thus may be used to characterize a disease. This may be in addition to a previous DMS that includes a previous version of the device or software, which, despite the use of older hardware/software, is still a solution for characterizing the disease.

The QP may involve an evidence-based validation process that enables improved lifecycle management of the TSP. These QPs represent standardized experiments that generate evidence that the overall solution from TSP is performed at a sufficient level for its intended purpose. In various embodiments, the QP is fully automated, e.g., for validating upgrade algorithms. Thus, the results of the upgrade algorithm may reference the available reference data set. In various embodiments, QP involves controlled experiments. For example, if a new upgrade device is released, the QP may involve evaluating the outcome of the upgrade device compared to the previous version of the device by using two devices on the patient population.

In particular embodiments, the QP is implemented to ensure that the upgraded TSP or DMS (e.g., due to the incorporation of the upgraded device or software) achieves comparable results as compared to the previous version of the TSP or DMS. In various embodiments, if the difference between the raw data is less than a threshold number, the raw data captured by the older version of the DMS device is comparable to the raw data captured by the newer version of the DMS device. In various embodiments, the threshold number is 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20%. In a specific embodiment, the threshold number is 10%. In a specific embodiment, the threshold number is 5%. In a specific embodiment, the threshold number is 2%. In various embodiments, if the difference between different meaningful health data is less than a threshold number, the meaningful health data transformed from the raw data captured by the older version of the DMS device is comparable to the meaningful health data transformed from the raw data captured by the newer version of the DMS new device. In various embodiments, the threshold number is 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20%. In a specific embodiment, the threshold number is 10%. In a specific embodiment, the threshold number is 5%. In a specific embodiment, the threshold number is 2%.

In various embodiments, when successfully verified using QP, either the TSP upgrade or the DMS upgrade may be annotated accordingly. For example, metadata of the TSP or DMS may be annotated with an indication that verification using QP was successfully performed. In various embodiments, the metadata may be accessed for inspection by a third party (e.g., through a catalog or marketplace). Thus, the third party can be easily notified of the TSP and/or DMS that has been successfully verified using QP.

To provide an example, TSPs assessed by a somatorecorder with daily life physical activity endpoint (coi=gait speed) cover many smart Watch devices (e.g., apple Watch 5, GENEActiv Original Watch, actiGraph GT9X Link). Over time, new smart watch devices (such as AppleWatch 6) release. QP is implemented to ensure that the results measured by the DMS remain valid due to device upgrades. An example of how this can be evaluated using a qualification protocol is as follows:

1. the participants of the group (n=20) wear both the old and the new devices (e.g., on either wrist-if a wrist-worn device). The participant may be a healthy individual or, alternatively, may comprise a patient population. For example, if there is no difference between a healthy participant and a patient population when measuring gait speed, the patient participant may be included as a participant.

2. Where the concept of interest is gait speed, participants are required to perform tasks related to physical activity, such as walking, running and climbing stairs, while wearing both the old and new devices.

3. The raw data is captured continuously for an extended period of time (e.g., 5 days) and the corresponding algorithm (same or new) converts the raw data set into a meaningful healthy data set with gait speed evidence.

4. If the converted health dataset is within a comparable range (e.g., < 2%), then both devices are verified as being comparable, and Apple Watch 6 can now be considered for the same research purposes as its old device. Conversely, if the QP is unable to validate the new device as generating a comparable result, the manufacturer may decide to re-evaluate the new device to ensure that it will be validated by the QP in a second evaluation.

In various embodiments, the new device release or the new software release may exceed the specification of the TSP. For example, assume that newly released Apple Watch 6 acquires raw data at a frequency range between 32Hz to 256 Hz. This may be beyond the specification of TSP (e.g., 1Hz to 100Hz frequency). Thus, a new TSP or an upgraded TSP incorporating a wider device specification (e.g., a wider frequency) may be generated and validated using QP.

Exemplary method

Construction of TSP or DMS

Fig. 3A is an example flow 305 for constructing a digital measurement solution according to one embodiment. Step 310 involves generating a measurement definition of the target solution profile that defines one or more concepts of interest associated with the disease. Step 315 involves generating or selecting a measurement instrument resource for the target solution profile, the measurement instrument resource configured to transform data captured according to the measurement definition into a dataset, such as a meaningful health dataset. In one scenario, the instrumentation resources in the different TSPs are previously generated, and thus, the instrumentation resources may be selected and reused here. In another scenario, a measurement instrument resource is generated from the head. Step 320 involves generating evidence resources of the target solution profile for performing one or more verifications on a dataset, such as a meaningful healthy dataset. Step 325 involves validating the target solution profile using a qualification protocol. Step 330 involves generating the DMS by specifying a device for at least the measurement instrument resource of the target solution profile. In various embodiments, step 330 further includes specifying a particular algorithm that transforms the raw data captured by the device into a meaningful health data set.

Characterization of diseases Using DMS

Fig. 3B is an example flow 350 for characterizing a disease in a subject using a Digital Measurement Solution (DMS), according to one embodiment. Although the flowchart in fig. 3B shows steps 355, 360, and 365 in this order, in various embodiments, steps 355, 360, and 365 can be ordered differently. For example, before obtaining the measurement of interest at step 355, a DMS may first be selected at step 360.

Step 355 involves obtaining a measurement of interest. Here, the measurement method specified by the digital measurement solution may be used to capture the measurement result of interest. For example, a particular device having specifications (e.g., data storage capacity, battery life, measurement frequency) specified in a digital measurement solution (e.g., in a measurement method element) may be used to capture measurements of interest.

Step 360 involves selecting a DMS from a plurality of DMS of a common class represented by the target solution profile. Here, the selected DMS specifies the measurement method used to capture the measurement result of interest in step 355.

Step 365 involves applying one or more elements of the DMS to the obtained measurements of interest to characterize the disease of the subject. For example, step 365 may involve applying an algorithm specified in the algorithm element of the DMS. The algorithm transforms raw data of the measurement of interest into a meaningful health dataset. Thus, the disease of the subject may be characterized according to a meaningful health dataset.

Providing TSP or DMS

FIG. 3C is an example flow 375 for providing a target solution profile or one or more digital measurement solutions, according to one embodiment. Step 380 involves providing a TSP directory. For example, step 380 may involve presenting the TSP directory in the marketplace to a third party so that the third party may access the TSP directory. In various embodiments, each TSP includes a measurement definition resource, a measurement instrument resource, and an evidence resource.

Step 385 involves receiving a selection of one or more TSPs in the directory. For example, the third party may select a TSP appropriate for its needs.

Step 390 involves providing the selected TSP, or one or more digital measurement solutions belonging to a common class represented by the selected TSP. In various embodiments, the selected TSP is provided. In various embodiments, one or more digital measurement solutions are provided.

Diseases and disorders

TSP and DMS constructed and implemented for a particular disease or disorder are disclosed herein. In various embodiments, the disease may be, for example, cancer, inflammatory disease, neurodegenerative disease, neurological disease, autoimmune disease, neuromuscular disease, metabolic disorder (e.g., diabetes), cardiac disease, or fibrotic disease.

In various embodiments, the cancer may be a lung bronchioloalveolar carcinoma (BAC), bladder cancer, a female genital tract malignancy (e.g., uterine serous carcinoma, endometrial carcinoma, vulvar squamous cell carcinoma, and uterine sarcoma), an ovarian surface epithelial carcinoma (e.g., ovarian clear cell carcinoma, epithelial ovarian carcinoma, fallopian tube carcinoma, and primary peritoneal carcinoma), breast cancer, non-small cell lung cancer (NSCLC), a male genital tract malignancy (e.g., testicular carcinoma), retroperitoneal or peritoneal carcinoma, gastroesophageal adenocarcinoma, esophageal gastric junction carcinoma, hepatocellular carcinoma, esophageal and esophageal gastric junction carcinoma, cervical cancer, cholangiocarcinoma, pancreatic adenocarcinoma, extrahepatic cholangiocarcinoma, small intestine malignancy, gastric adenocarcinoma, unknown primary Carcinoma (CUP), colorectal adenocarcinoma, esophageal carcinoma, prostate adenocarcinoma, renal carcinoma, head and neck squamous cell carcinoma, thymus carcinoma, non-melanoma skin carcinoma, thyroid carcinoma (e.g., papillary carcinoma), head and neck cancer, non-epithelial ovarian carcinoma (non-EOC), uveal melanoma, malignant carcinoma, carcinoma of the central nervous system (c), cancer of the small cell endocrine System (SCLC), a tumor of any nervous system, and a tumor of the nervous system. For example, in certain embodiments, the cancer is breast cancer, non-small cell lung cancer, bladder cancer, kidney cancer, colon cancer, and melanoma.

In various embodiments, the inflammatory disease may be any of the following: acute Respiratory Distress Syndrome (ARDS), acute Lung Injury (ALI), alcoholic liver disease, allergic inflammation of the skin, lungs and gastrointestinal tract, allergic rhinitis, ankylosing spondylitis, asthma (allergic and non-allergic), atopic dermatitis (also known as atopic eczema), atherosclerosis, celiac disease, chronic Obstructive Pulmonary Disease (COPD), pulmonary Arterial Hypertension (PAH), chronic Respiratory Distress Syndrome (CRDS), colitis, dermatitis, diabetes mellitus, eczema, endocarditis, fatty liver disease, fibrosis (e.g., idiopathic pulmonary fibrosis, scleroderma, renal fibrosis and scarring), food allergies (e.g., allergies to peanuts, eggs, milk, shellfish, tree nuts, etc.), gastritis, gout, liver steatosis, hepatitis, body organ inflammation (including joint inflammation, including joints of the knee, limb or hand), inflammatory Bowel Disease (IBD) (including crohn's disease or ulcerative colitis), intestinal hyperplasia, irritable bowel syndrome, juvenile rheumatoid arthritis, liver disease, metabolic syndrome, multiple sclerosis, myasthenia gravis, neurogenic pulmonary edema, nephritis (e.g., glomerulonephritis), non-alcoholic fatty liver disease (NAFLD) (including non-alcoholic steatosis and non-alcoholic steatohepatitis (NASH)), obesity, prostatitis, psoriasis, psoriatic arthritis, rheumatoid Arthritis (RA), sarcoidosis, sinusitis, splenitis, seasonal allergies, sepsis, systemic lupus erythematosus, uveitis and ultraviolet-induced dermatitis.

In various embodiments, the neurodegenerative disease may be any of alzheimer's disease, parkinson's disease, traumatic CNS injury, down's Syndrome (DS), glaucoma, amyotrophic Lateral Sclerosis (ALS), frontotemporal dementia (FTD), and huntington's disease. In addition, the neurodegenerative disease may further include: deficiency of the transparent compartment, acid lipase disease, acid maltase deficiency, acquired epileptic aphasia, acute disseminated encephalomyelitis, ADHD, addison's pupil, addison's syndrome, adrenoleukodystrophy, corpus callosum hypoplasia, cognitive disability, ai Kaer syndrome, AIDS, alexander's disease, alter's disease, alternant hemiplegia, congenital brain deformity, aneurysms, angel's syndrome, angiomatosis, hypoxia, antiphospholipid syndrome, aphasia, arachnoid cyst, arachnoiditis, alnod-base's deformity, arterial vein deformity, alberg syndrome, ataxia telangiectasia, ataxia and cerebellar or spinocerebellar degeneration, autism, dysfunctions, bass syndrome, bei Duishi, beckel's muscle rigidity, behcet's disease Bell's palsy, benign idiopathic blepharospasm, benign focal muscular atrophy, benign intracranial hypertension, ber-Luo Ershi syndrome, bischwang's disease, blepharospasm, buacher-Su Ciba syndrome, brachial plexus injury, burab-Eggerton syndrome, brain or spinal tumors, cerebral aneurysms, brain injury, brown-Seger syndrome, globinotropic muscular atrophy, leukoencephalopathy, canavan's disease, causalgia, cavernous hemangioma, spongiform hemangioma, central spinal cord syndrome, central pain syndrome, pontine central myelinolysis, head diseases, ceramidase deficiency, cerebellar degeneration, cerebroplasia, cerebral aneurysms, cerebral arteriosclerosis, brain atrophy, brain type beriberi, brain giant, brain tissue hypoxia, cerebral paralysis, brain ocular surface skeletal syndrome, cerebral palsy, fibular amyotrophic lateral sclerosis, subtonsillar hernia deformity, chorea, chronic Inflammatory Demyelinating Polyneuropathy (CIDP), kopalsy syndrome, cavitary brain, congenital facial paralysis, congenital myasthenia, congenital myopathy, basal ganglia degeneration, craniofacial arteritis, craniocerebral suture early closure, cruz Fode-Jack disease, cumulative injury disorder, cushing's syndrome, giant cell inclusion body disease, ocular and foot chorea DANSHEN, RELATER, DONGRENSHEN, dementia with lewy bodies, dentate nucleus and cerebellar ataxia, dentate nucleus and red nucleus atrophy, dermatomyositis, developmental disturbance of use, devickers syndrome diabetic neuropathy, diffuse sclerosis, deravigde syndrome, autonomic dysfunction, writing difficulties, dyslexia, dysphagia, myoclonus cerebellar dysordination, dystonia, dyslexia, dystonia, dyslexia, and dysuria early infant epileptic encephalopathy, empty pterocarb syndrome, encephalitis, epidemic encephalitis, cerebral distention, encephalopathy, cerebral trigeminal angiomatosis, epilepsy, el b-duel paralysis and dejielin-k Lv Pukai paralysis, el b-paralysis, essential tremor, extrabridge myelination, fabry disease, fire syndrome, syncope, familial autonomic dysregulation, familial angioma, familial periodic paralysis, familial spasticity, fabry's disease, febrile convulsion, fibromuscular dysplasia, fischer syndrome, infant hypomyotonia syndrome, gait abnormality, friedel's ataxia, frontotemporal dementia, ganglioside deposition, gaucher's disease, german's syndrome, german-schlemn Shen Kezeng syndrome, giant cell arteritis, giant cell culvert disease, cytoleukodystrophy, glossopharyngeal neuralgia, glycogen storage disease, green-barre syndrome, harwy-Schpalz disease, head injury, continuous migraine, hemifacial spasm, cross hemiplegia, hereditary neuropathy, hereditary spastic paraplegia, polyneuritis type hereditary movement disorder, shingles, ping Shan syndrome, fulmus-Ai Dizeng syndrome, forebrain craving deformity, HTLV-1-associated myelopathy, house syndrome, huntington's disease, water-free brain, cerebral hydrops, spinal cord hydrops, hyper-rhythmic syndrome, somnolence, hypertone, hypotonia, hypoxia, immune-mediated encephalomyelitis, inclusion body myositis, pigment imbalance, infantile muscular hypotonia, infantile neuroaxial dystrophy, infant phytanic acid storage disease, infant anti-gastric disease, infantile spasticity, infantile dystonia, infantile mental hypotonia inflammatory myopathy, split pillow brain deformity, intestinal-derived lipodystrophy, intracranial cyst, intracranial hypertension, isaax syndrome, zhu Bate syndrome, karens-Sail syndrome, kennedy's disease, kinberg's syndrome, crylen-Lavender syndrome, creper-Fisher syndrome, crylen-Ternner syndrome (KTS), crylen-Buxi syndrome, colesakoff's amnesia syndrome, kerabi disease, coulter-Wende disease, kolu's disease, lanbert-Eaton's muscle weakness syndrome, landold-Crylen's syndrome, bulbar outside syndrome, learning disorder, lewy disease, levone Norke-Gastro syndrome, levonihan syndrome, white matter dystrophy, levender-Kerr syndrome, lewy body dementia, lipid storage disease, lipid protein deposition, agamohair deformity, atresia syndrome, rugilles de la disease, lupus, lyme disease, machado-joseph disease, megabrain, melke's pine-rosento syndrome, meningitis, portal disease, paresthesia thigh pain, metachromatic white matter dystrophy, microcephaly, migraine, milfewax syndrome, small stroke, mitochondrial myopathy, motor neuron disease, smog disease, viscolipid storage disease, mucopolysaccharidosis, multiple Sclerosis (MS), multiple system atrophy, muscular atrophy, myasthenia gravis, myoclonus, myopathy, myotonic, narcolepsy, neurosis, neural acanthocytosis, neurodegeneration with brain iron accumulation, neurofibromatosis, nerve blocker malignancy, nervous system sarcoidosis, neurotoxicity, spongiform mole, niemann-pick disease, mucopolysaccharide storage disease non-24 hour sleep-wake disorders, normal pressure hydrocephalus, occipital neuralgia, occult spinal dysplasia sequences, large-area primary syndrome, olivopontocerebellar atrophy, strabismus myoclonus, orthostatic hypotension, oxalis-Maclaude syndrome, overuse syndrome, pantothenate kinase-associated neurodegeneration, carcinoid syndrome, paresthesia, parkinson's disease, paroxysmal chorea and hand-foot bradykinesia, paroxysmal migraine, paris-Luo Mba lattice syndrome, peli-Mez's disease, peri-nerve cyst, periodic paralysis, peripheral neuropathy, ventricular Zhou Ruanhua disease, complex mental dysplasia disorders, nerve compression, piriform muscle syndrome, nerve plexus lesions, polymyositis, pompe disease, brain punch-through, postherpetic neuralgia, post-infection encephalomyelitis, poliomyelitis sequelae, peri, posture hypotension, posture tachycardia syndrome (POTS), primary lateral sclerosis, prion diseases, progressive multifocal leukoencephalopathy, progressive sclerotic gray dystrophy, progressive supranuclear palsy, facial agnosis, pseudobrain tumors, lambda Ji Hengte syndrome I, lambda Ji Hengte syndrome II, lambda Mu Sen encephalitis, reflex sympathetic nerve syndrome Raffinose disease, repetitive movement disorder, repetitive stress injury, restless leg syndrome, retrovirus-associated myelopathy, rattt syndrome, lei Yishi syndrome, rheumatic encephalitis rili-dydrome, san viterbi chorea, sandhoff's disease, cerebral cleft deformity, dysplasia of the visual septum, shingles, schrader's syndrome, sjogren's syndrome, sleep apnea, comatose, sotos syndrome, spasticity, spinal cord infarction, spinal cord injury, spinal cord tumor, spinal cerebellar atrophy, spinal cerebellar degeneration, stiff person syndrome, striated substantia degeneration, stroke, stethod-weber syndrome, transient unilateral neuralgia-like headache with conjunctival congestion and lacrimation (SUNCT) headache, syncope, syphilitic myelosclerosis, syringomy, spinal cord tuberculosis, tardive dyskinesia, tarofen cyst, saxophone, temporal arteritis, spinal cord embolic syndrome, touretsen's myotonic, thoraco outlet syndrome, thyrotoxic myopathy, tinnitus, toddar paralysis, tourette syndrome, transient ischemic attacks, infectious spongiform encephalopathy, transverse myelitis, traumatic brain injury, tremor, trigeminal neuralgia, tropical spastic paralysis, turyardstick syndrome, tuberous sclerosis, vasculitis (including temporal arteritis), fengai family nomomo disease, fengxi peter-Lin Daobing (VHL), von willebrand Lin Huozeng disease, valencburg syndrome, valdham's disease, valpick-kessakoff syndrome, westerson's syndrome, whip injury, whip disease, wilson's syndrome, wilson's disease, walman's disease, X-linked spinal cord and bulbar muscular atrophy, and Ji Weige syndrome.

In various embodiments, the autoimmune disease or disorder may be any of the following: arthritis, including rheumatoid arthritis, acute arthritis, chronic rheumatoid arthritis, gout or gouty arthritis, acute immune arthritis, chronic inflammatory arthritis, degenerative arthritis, collagen type II-induced arthritis, infectious arthritis, lyme arthritis, proliferative arthritis, psoriatic arthritis, steve's disease, spondyloarthritis, juvenile onset rheumatoid arthritis, osteoarthritis, chronic primary polyarthritis, reactive arthritis, and ankylosing spondylitis; inflammatory hyperproliferative skin disorders; psoriasis, such as plaque psoriasis, pustular psoriasis and nail psoriasis; specific reactivity, including atopic diseases such as pollinosis and Qiao Buzeng syndrome; dermatitis including contact dermatitis, chronic contact dermatitis, exfoliative dermatitis, allergic contact dermatitis, dermatitis herpetiformis, dermatitis nummulosa, seborrheic dermatitis, non-atopic dermatitis, primary irritant contact dermatitis, and atopic dermatitis; x-linked high IgM syndrome; allergic intraocular inflammatory diseases; urticaria, such as chronic allergic urticaria, chronic idiopathic urticaria, and chronic autoimmune urticaria; myositis; polymyositis/dermatomyositis; juvenile dermatomyositis; toxic epidermis necrosis; scleroderma, including systemic scleroderma; sclerosis, such as systemic sclerosis, multiple Sclerosis (MS), spinal-optic nerve MS, primary Progressive MS (PPMS), relapsing Remitting MS (RRMS), progressive systemic sclerosis, atherosclerosis, arteriosclerosis, disseminated sclerosis and ataxia-type sclerosis; neuromyelitis optica (NMO); inflammatory Bowel Diseases (IBD), including crohn's disease, autoimmune mediated gastrointestinal disease, colitis, ulcerative colitis, microscopic colitis, collagenous colitis, polypoidal colitis, necrotizing enterocolitis, transmural colitis, and autoimmune inflammatory bowel disease; inflammation of the intestinal tract; pyoderma gangrenosum; erythema nodosum; primary sclerosing cholangitis; respiratory distress syndrome, including adult or Acute Respiratory Distress Syndrome (ARDS); meningitis; all or part of the uvea is inflamed; iritis; choroiditis; autoimmune blood disease; rheumatoid spondylitis; rheumatoid synovitis; hereditary angioedema; cranial nerve injury, such as in meningitis; herpes progenitalis; gestational pemphigoid; scrotum itching; autoimmune premature ovarian failure; sudden hearing loss due to autoimmune disorders; igE-mediated diseases such as allergy, allergic rhinitis and atopic rhinitis; encephalitis such as Las Mu Senshi encephalitis and limbic encephalitis and/or brain stem encephalitis; uveitis, such as anterior uveitis, acute anterior uveitis, granulomatous uveitis, non-granulomatous uveitis, phacoenogenic uveitis, posterior uveitis, or autoimmune uveitis; glomerulonephritis (GN) with and without nephrotic syndrome, such as chronic or acute glomerulonephritis, primary GN, immune-mediated GN, membranous GN (membranous nephropathy), idiopathic membranous GN or idiopathic membranous nephropathy, membranous or Membranous Proliferative GN (MPGN), including types I and II, and fast-progressing GN; proliferative nephritis; autoimmune multiple gland endocrine failure; balanitis, including plasma cell localized balanitis; balanus dermatitis; a central annular erythema; permanent pigment abnormal erythema; erythema multiforme; granuloma annulare; lichen glossing; lichen atrophic sclerosus; chronic simple moss; moss of small acantha; moss planus; lamellar ichthyosis; epidermolytic hyperkeratosis; pre-cancerous keratosis; pyoderma gangrenosum; allergic conditions and responses; an allergic reaction; eczema, including allergic or atopic eczema, seborrheic eczema, dyshidrosis eczema and vesicular palmoplantar eczema; asthma such as bronchial asthma and autoimmune asthma; disorders involving T cell infiltration and chronic inflammatory responses; immune responses to foreign antigens (such as fetal a-B-O blood group) during pregnancy; chronic pulmonary inflammatory disease; autoimmune myocarditis; a leukocyte adhesion defect; lupus, including lupus nephritis, lupus encephalitis, pediatric lupus, non-renal lupus, extra-renal lupus, discoid lupus and discoid lupus erythematosus, alopecia lupus, systemic Lupus Erythematosus (SLE), cutaneous SLE, subacute cutaneous SLE, neonatal lupus syndrome (NLE), and disseminated lupus erythematosus; juvenile onset (type I) diabetes mellitus, including pediatric Insulin Dependent Diabetes Mellitus (IDDM), adult onset diabetes mellitus (type II diabetes), autoimmune diabetes, idiopathic diabetes insipidus, diabetic retinopathy, diabetic nephropathy, and diabetic macroarterial disease; immune responses associated with acute and delayed-type hypersensitivity mediated by cytokines and T lymphocytes; tuberculosis; sarcoidosis; granulomatosis, including lymphomatoid granulomatosis; wegener's granulomatosis; granulocytopenia; vasculitis, including vasculitis, macrovasculitis, polymyalgia rheumatica and giant cell (high ampere) arteritis, medium vasculitis, kawasaki disease, polyarteritis nodosa/peri-nodular arteritis, polyarteritis microscopically, immunovasculitis, CNS vasculitis, cutaneous vasculitis, hypersensitivity vasculitis, necrotizing vasculitis, systemic necrotizing vasculitis, ANCA-related vasculitis, chager-scht's vasculitis or syndrome (CSS) and ANCA-related small vasculitis; temporal arteritis; aplastic anemia; autoimmune aplastic anemia; coomassie positive anemia; dai Mengde-Blackifen anemia; hemolytic anemia or immune hemolytic anemia, including autoimmune hemolytic anemia (AIHA), pernicious anemia; edison's disease; pure red blood cell anemia or aplastic anemia (PRCA); factor VIII deficiency; hemophilia a; autoimmune neutropenia; whole blood cytopenia; leukopenia; diseases involving leukocyte exudation; inflammatory diseases of the CNS; multiple organ injury syndromes, such as those secondary to sepsis, trauma, or hemorrhage; antigen-antibody complex mediated diseases; anti-glomerular basement membrane diseases; antiphospholipid antibody syndrome; allergic neuritis; behcet's disease/syndrome; kalman syndrome; goodebas syndrome; raynaud's syndrome; sjogren's syndrome; stevens-Johnson syndrome; pemphigoid such as bullous and cutaneous pemphigoid, pemphigus vulgaris, pemphigus largescens, pemphigus myxoid membrane pemphigoid and erythroid pemphigoid; autoimmune polycycloadenosis; lyte's disease or syndrome; thermal damage; preeclampsia; immune complex disorders such as immune complex nephritis and antibody-mediated nephritis; multiple neuropathy; chronic neuropathies such as IgM polyneuropathy and IgM mediated neuropathies; thrombocytopenia (e.g., developed from patients with myocardial infarction), including Thrombotic Thrombocytopenic Purpura (TTP), post-transfusion purpura (PTP), heparin-induced thrombocytopenia, autoimmune or immune-mediated thrombocytopenia, idiopathic Thrombocytopenic Purpura (ITP), and chronic or acute ITP; scleritis, such as idiopathic cornea-scleritis, and episcleritis; autoimmune diseases of the testes and ovaries, including autoimmune orchitis and ovaries; primary hypothyroidism; hypoparathyroidism; autoimmune endocrine disorders including thyroiditis, autoimmune thyroiditis, hashimoto's disease, chronic thyroiditis (hashimoto's thyroiditis) or subacute thyroiditis, autoimmune thyroiditis, idiopathic hypothyroidism, graves ' disease, polyadenopathy, autoimmune polyadenopathy and polyadenoendocrinopathy syndrome; secondary tumor syndromes, including neuropathic secondary tumor syndromes; lambert-eaton muscle weakness syndrome or eaton-lambert syndrome; stiff person syndrome; encephalomyelitis, such as allergic encephalomyelitis and Experimental Allergic Encephalomyelitis (EAE); myasthenia gravis, such as the chest adenoma-associated myasthenia gravis; cerebellar degeneration; neuromuscular rigidity; strabismus myoclonus or strabismus myoclonus syndrome (OMS); sensory neuropathy; multifocal motor neuropathy; sheehan's syndrome; hepatitis, including autoimmune hepatitis, chronic hepatitis, lupus-like hepatitis, cytomegalohepatitis, chronic active hepatitis, and autoimmune chronic active hepatitis; lymphoid Interstitial Pneumonia (LIP); bronchiolitis obliterans (non-transplant) versus NSIP; lattice-barbier syndrome; primary grignard disease (IgA nephropathy); idiopathic IgA nephropathy; linear IgA skin disease; acute febrile neutrophilic dermatosis; impetigo under the stratum corneum; transient acanthosis skin disorders; cirrhosis, such as primary biliary cirrhosis and pulmonary cirrhosis; autoimmune enteropathy syndrome; celiac disease or celiac disease; stomatitis diarrhea (gluten bowel disease); refractory steatorrhea; idiopathic steatorrhea; cryoglobulinemia; amyotrophic lateral sclerosis (ALS; lou Gehrig disease); coronary artery disease; autoimmune ear diseases, such as Autoimmune Inner Ear Disease (AIED); autoimmune hearing loss; polychondritis, such as refractory or recurrent polychondritis; alveolar proteinosis; crohn's disease/non-syphilis interstitial keratitis; bell palsy; sweet disease/syndrome; autoimmune rosacea; pain associated with shingles; amyloidosis; non-cancerous lymphocytosis; primary lymphopenia, including monoclonal B-cell lymphopenia (e.g., benign monoclonal gammaglobulopathy and unidentified monoclonal gammaglobulopathy, MGUS); peripheral neuropathy; ion channel disorders such as epilepsy, migraine, arrhythmia, muscular disorders, deafness, blindness, periodic paralysis, and ion channel disorders of the CNS; autism; inflammatory myopathy; focal or segmental or Focal Segmental Glomerulosclerosis (FSGS); endocrine ocular diseases; uveal retinitis; chorioretinitis; autoimmune liver dysfunction; fibromyalgia; multiple endocrine failure; schmidt syndrome; adrenalitis; gastric atrophy; alzheimer's disease; demyelinating diseases such as autoimmune demyelinating diseases and chronic inflammatory demyelinating polyneuropathy; de leisler syndrome; alopecia areata; alopecia totalis; CREST syndrome (calcaneal deposition, raynaud's phenomenon, esophageal dyskinesia, digital end sclerosis and telangiectasia); male and female autoimmune sterility (e.g., due to anti-sperm antibodies); mixed connective tissue disease; right gauss disease; rheumatic fever; recurrent abortion; farmer's lung; erythema multiforme; post-cardiotomy syndrome; cushing's syndrome; lung of bird feeders; allergic granulomatous vasculitis; benign lymphocytic vasculitis; alport syndrome; alveolitis, such as allergic alveolitis and fibrotic alveolitis; interstitial lung disease; blood transfusion reaction; leprosy; malaria; sammt syndrome; kaplan syndrome; endocarditis; endocardial myocardial fibrosis; diffuse pulmonary interstitial fibrosis; interstitial pulmonary fibrosis; pulmonary fibrosis; idiopathic pulmonary fibrosis; cystic fibrosis; endophthalmitis; persistent raised erythema; fetal erythroblast disease; eosinophilic fasciitis; schulman syndrome; fei Erdi syndrome; filariasis; ciliary inflammation such as chronic ciliary inflammation, heterochronic ciliary inflammation, iridocyclitis (acute or chronic), or Fuchs ciliary inflammation; hennuo-schonlein purpura; sepsis; endotoxemia; pancreatitis; thyrotoxicosis; emmens syndrome; autoimmune gonadal failure; siendenhame chorea; nephritis after streptococcus infection; thromboangiitis obliterans; thyrotoxicosis; tuberculosis of spinal cord; choroiditis; megacytokinesis pain; chronic hypersensitivity pneumonitis; keratoconjunctivitis sicca; epidemic keratoconjunctivitis; idiopathic nephrotic syndrome; micro-pathologic kidney disease; benign familial and ischemia reperfusion injury; reperfusion of transplanted organs; retinal autoimmunity; inflammation of the joints; bronchitis; chronic obstructive airways/lung disease; silicosis; aphtha; aphthous stomatitis; arteriosclerotic diseases; seminiferous disorders (aspermiogenese); autoimmune hemolysis; primary disease; cryoglobulinemia; dupuytren's contracture; intraocular lens allergy; allergic enteritis; leprosy nodular erythema; idiopathic facial paralysis; fever myalgia; huffman-Ridge disease; sensory nerve hearing loss; paroxysmal hemoglobinuria; hypogonadism; regional ileitis; leukopenia; infectious mononucleosis; transverse myelitis; primary idiopathic myxoedema; non-inflammatory kidney disease; sympathogenic ophthalmia; granulomatous orchitis; pancreatitis; acute polyneuritis; pyoderma gangrenosum; quiz thyroiditis; acquired spleen atrophy; non-malignant thymoma; vitiligo; a toxic shock syndrome; food poisoning; disorders involving T cell infiltration; a leukocyte adhesion defect; immune responses associated with acute and delayed-type hypersensitivity mediated by cytokines and T lymphocytes; diseases involving leukocyte exudation; multiple organ injury syndrome; antigen-antibody complex mediated diseases; anti-glomerular basement membrane diseases; allergic neuritis; autoimmune polycycloadenosis; oophoritis; primary myxoedema; autoimmune atrophic gastritis; sympathogenic ophthalmia; rheumatic diseases; mixed connective tissue disease; nephrotic syndrome; insulitis; multiple endocrine failure; type I autoimmune polyadenopathy syndrome; adult onset idiopathic parathyroid hypofunction (AOIH); cardiomyopathy, such as dilated cardiomyopathy; acquired Epidermolysis Bullosa (EBA); hemochromatosis; myocarditis; nephrotic syndrome; primary sclerosing cholangitis; suppurative or non-suppurative sinusitis; acute or chronic sinusitis; ethmoid sinusitis, frontal sinusitis, maxillary sinusitis, or sphenoid sinusitis; eosinophil-related diseases such as eosinophilia, pulmonary infiltration eosinophilia, eosinophilia-myalgia syndrome, lv-Fr syndrome, chronic eosinophilic pneumonia, tropical pulmonary eosinophilia, bronchopneumonia aspergillosis, or eosinophil-containing granuloma; allergic reactions; seronegative spondyloarthritis; multiple endocrine autoimmune diseases; sclerosing cholangitis; chronic cutaneous mucosal candidiasis; bruton's syndrome; transient hypogammaglobulinemia in infancy; weiscott-Aldrich syndrome; ataxia telangiectasia syndrome; vasodilation; autoimmune diseases associated with collagen diseases, rheumatism, neurological diseases, lymphadenitis, reduced blood pressure response, vascular dysfunction, tissue damage, cardiovascular ischemia, hyperalgesia, renal ischemia, cerebral ischemia and diseases accompanied by angiogenesis; allergic hypersensitive disease; glomerulonephritis; reperfusion injury; ischemic reperfusion disorder; reperfusion injury of myocardial tissue or other tissue; lymphomatous tracheobronchitis; inflammatory skin diseases; dermatological disorders with an acute inflammatory component; multiple organ failure; bullous disease; necrosis of renal cortex; acute suppurative meningitis or other central nervous system inflammatory diseases; ocular and orbital inflammatory diseases; granulocyte transfusion-associated syndrome; cytokine-induced toxicity; narcolepsy; acute severe inflammation; chronic refractory inflammation; pyelitis; intraarterial hyperplasia; peptic ulcer; heart valve inflammation; and endometriosis. In particular embodiments, the autoimmune disease of the subject may include one or more of the following: systemic Lupus Erythematosus (SLE), lupus nephritis, chronic graft versus host disease (cGVHD), rheumatoid Arthritis (RA), sjogren's syndrome, vitiligo, inflammatory bowel disease, and crohn's disease. In a specific embodiment, the autoimmune disease is Systemic Lupus Erythematosus (SLE). In a specific embodiment, the autoimmune disease is rheumatoid arthritis.

Exemplary metabolic disorders include, for example, diabetes, insulin resistance, lysosomal storage disorders (e.g., gaucher's disease, krabbe disease, niemann-pick disease types a and B, multiple sclerosis, fabry's disease, tazicar's disease, and sandhoff A, B variants), obesity, cardiovascular disease, and dyslipidemia. Other exemplary metabolic diseases include, for example: 17-alpha-hydroxylase deficiency, 17-beta hydroxysteroid dehydrogenase 3 deficiency, 18 hydroxylase deficiency, 2-hydroxyglutarate urine, 2-methylbutyl-CoA dehydrogenase deficiency, 3-alpha-hydroxyacyl-CoA dehydrogenase deficiency, 3-hydroxyisobutyric acid urine, 3-methylcrotonyl-CoA carboxylase deficiency, 3-methylglutaryl-CoA hydratase deficiency (AUH deficiency), 5-hydroxyproline enzyme deficiency, 6-pyruvoyl tetrahydropterin synthase deficiency, abdominal obesity metabolic syndrome, beta-free lipoproteinemia, no thixotropic enzyme blood, plasmepelanin deficiency, acetyl-CoA acetyltransferase 2 deficiency, acetylcarnitine deficiency, intestinal acrodermatitis, adenine phosphoribosyl transferase deficiency, adenosine deaminase deficiency, adenosine monophosphate deaminase 1 deficiency adenylosuccinase deficiency, adrenomyeloneuropathy, adult polyglucose disorder, albino deafness syndrome, urine black urine disorder, alpeh's syndrome, alpha-1 antitrypsin deficiency, alpha-ketoglutarate dehydrogenase deficiency, alpha-mannosidosis, aminoacylase 1 deficiency, iron-particle young erythrocyte anemia and spinocerebellar ataxia, arginase deficiency, argininosuccinuria, aromatic L-amino acid decarboxylase deficiency, joint contracture-renal dysfunction-cholestasis syndrome, arts syndrome, aspartylglucosamine urine disorder, atypical gaucher disease caused by sphingolipid activated protein C deficiency, autoimmune polyadenylic syndrome type 2, autosomal dominant optic atrophy and cataracts, autosomal erythropoiesis protoporphyrin, autosomal recessive spasticity type 4, bass syndrome, butt syndrome prenatal type 1, butt syndrome prenatal type 2, butt syndrome type 3, butt syndrome type 4, beta-ketothiolase deficiency, biotin deficiency, bijohn's syndrome, carbamoyl phosphate synthase 1 deficiency, carnitine palmitoyl transferase 1A deficiency, carnitine-acyl carnitine translocase deficiency, carnosine blood, central diabetes insipidus, cerebral folate deficiency, brain tendinosis, neuronal ceroid lipofuscinosis type 1, cha Nalin-Duffmann syndrome, duck-east syndrome, childhood phosphatase hypovolemia, cholesteryl ester deposition, chondrocals, chylomicron retention, citrulline transport deficiency, and the like congenital bile acid synthesis deficiency type 2, crigler-na Gu Erzeng syndrome, cytochrome C oxidase deficiency, D-2-hydroxyglutarate, D-bifunctional protein deficiency, D-glyceridemia, daruna disease, dicarboxylic acid amino acid urine, dihydropteridine reductase deficiency, dihydropyrimidase deficiency, diabetes insipidus, dopamine beta hydroxylase deficiency, dawn-degos disease, erythropoiesis uroporphyria associated with bone marrow malignancy, familial chylomicronemia syndrome, familial HDL deficiency, familial hypercalcemia type 1, familial hypercalcemia type 2, familial hypercalcemia type 3, familial LCAT deficiency, familial partial lipodystrophy type 2, vancouni-peck syndrome, fabry disease, fructose-1, 6-bisphosphatase deficiency, gamma-cystathionine deficiency, gaucher disease, gilbert syndrome, gettman syndrome, glucose transporter type 1 deficiency syndrome, glutamine deficiency, congenital glutarate, glutathione synthase deficiency, glycine-N-methyltransferase deficiency, glycogen storage disease, liver lipase deficiency, hyperhomocysteinemia, hull syndrome, hyperglyceridemia, irbescend-Grasbeck syndrome, iminoglyciuria, childhood neuroaxial dystrophy, kidney-Seer syndrome, crabb disease, lactate dehydrogenase deficiency, let Shi Nihan syndrome, mentha's disease, methionine adenosyltransferase deficiency, mitochondrial complex deficiency, muscle phosphorylase kinase deficiency neuronal cerebroside deposition, niman-Picke disease type A, niman-Picke disease type B, niman-Picke disease type C1, niman-Picke disease type C2, ornithine transcarbamylase deficiency, pearson syndrome, bordetella syndrome, phosphoribosyl pyrophosphate synthase hyperactivity, primary carnitine deficiency, hyperoxaluria, purine nucleoside phosphorylase deficiency, pyruvate carboxylase deficiency, pyruvate dehydrogenase complex deficiency, pyruvate dehydrogenase phosphatase deficiency, pyruvate kinase deficiency, rafmm's disease, diabetes, xie Yizeng syndrome, senger's syndrome, sialidosis, sjogren-Lasone syndrome, tasa's two, cobalamin transfer protein 1 deficiency, trehalase deficiency, wolbber's disease, wolfram syndrome and Wolman disease.

Non-transitory computer readable medium

Also provided herein is a computer-readable medium comprising computer-executable instructions configured to implement any of the methods described herein. In various embodiments, the computer-readable medium is a non-transitory computer-readable medium. In some embodiments, the computer-readable medium is part of a computer system (e.g., a memory of a computer system). The computer-readable medium may include computer-executable instructions for performing the methods disclosed herein, such as methods for constructing, maintaining, implementing, and providing standardized solutions (e.g., DMS and TSP).

Computing device

In some embodiments, the above-described methods (including methods of constructing, maintaining, implementing, and providing standardized solutions (e.g., DMS and TSP)) are performed on a computing device. Examples of computing devices may include personal computers, desktop computers, laptop computers, server computers, intra-cluster computing nodes, message processors, hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, tablet computers, pagers, routers, switches, and the like.

Fig. 4 illustrates an example computing device 400 for implementing the systems and methods described in fig. 1A-1B, 2A-2F, and 3A-3C. In some implementations, the computing device 400 includes at least one processor 402 coupled to a chipset 404. The chipset 404 includes a memory controller hub 420 and an input/output (I/O) controller hub 422. Memory 406 and graphics adapter 412 are coupled to memory controller hub 420, and display 418 is coupled to graphics adapter 412. Storage 408, input interface 414, and network adapter 416 are coupled to I/O controller hub 422. Other embodiments of computing device 400 have different architectures.

Storage 408 is a non-transitory computer-readable storage medium such as a hard disk drive, compact disk read-only memory (CD-ROM), DVD, or solid state memory device. Memory 406 holds instructions and data used by processor 402. The input interface 414 is a touch screen interface, a mouse, a trackball or other type of input interface, a keyboard, or some combination thereof, for inputting data into the computing device 400. In some implementations, the computing device 400 may be configured to receive input (e.g., commands) from the input interface 414 via gestures from a user. Graphics adapter 412 displays images and other information on display 418. As one example, the display 418 may show a catalog of standardized solutions (e.g., DMS and/or TSP). The network adapter 416 couples the computing device 400 to one or more computer networks.

Computing device 400 is adapted to execute computer program modules to provide the functionality described herein. As used herein, the term "module" refers to computer program logic for providing specified functionality. Thus, modules may be implemented in hardware, firmware, and/or software. In one embodiment, program modules are stored on storage device 408, loaded into memory 406, and executed by processor 402.

The type of computing device 400 may vary from the embodiments described herein. For example, computing device 400 may lack some of the components described above, such as graphics adapter 412, input interface 414, and display 418. In some implementations, the computing device 400 may include a processor 402 for executing instructions stored on a memory 406.

In various embodiments, the different entities depicted in fig. 1A and/or 1B may implement one or more computing devices to perform the methods described above. For example, the digital solution system 130, the third party entity 110A, and the third party entity 110B may each employ one or more computing devices. As another example, one or more modules of the digital solution system 130 (e.g., the resource module 140, the target solution profile module 145, the digital measurement solution module 150, the lifecycle management module 155, the disease characterization module 160, and the marketplace module 165) may be implemented by one or more computing devices to perform the methods described above.

The methods of constructing, maintaining, implementing, and providing TSPs and/or DMS may be implemented in hardware, software, or a combination of both. In one embodiment, a non-transitory machine-readable storage medium, such as one of the above, is provided that includes a data storage material encoded with machine-readable data that, when using a machine programmed with instructions for using the data, is capable of performing the methods disclosed herein, including methods of constructing, maintaining, implementing, and providing TSPs and/or DMS. Embodiments of the above-described methods may be implemented in computer programs executing on programmable computers comprising a processor, a data storage system (including volatile and non-volatile memory and/or storage elements), a graphics adapter, an input interface, a network adapter, at least one input device, and at least one output device. A display is coupled to the graphics adapter. Program code is applied to the input data to perform the functions described above and generate output information. The output information is applied to one or more output devices in a known manner. The computer may be, for example, a personal computer, a microcomputer or a workstation of conventional design.

Each program can be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the programs may be implemented in assembly or machine language, as desired. In any case, the language may be a compiled or interpreted language. Each such computer program is preferably stored on a storage medium or device (e.g., ROM or magnetic disk) readable by a general or special purpose programmable computer, such that when the storage medium or device is read by the computer, the computer is configured and operated to perform the processes described herein. The system may also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform the functions described herein.

Signature patterns and their databases may be provided in a variety of media to facilitate their use. "media" refers to articles containing the signature pattern information of the present invention. The database of the present invention may be recorded on a computer readable medium (e.g., any medium that can be directly read and accessed by a computer). Such media include, but are not limited to: magnetic storage media such as floppy disks, hard disk storage media, and magnetic tape; an optical storage medium such as a CD-ROM; an electrical storage medium such as RAM and ROM; and mixtures of these categories, such as magnetic/optical storage media. One of ordinary skill in the art can readily understand how any of the currently known computer readable media can be used to create an article of manufacture containing records of the database information of the present invention. "recorded" refers to a process for storing information on a computer readable medium using any such method known in the art. Any convenient data storage structure may be selected based on the means for accessing the stored information. A variety of data processor programs and formats may be used for storage, e.g., word processing text files, database formats, etc.

Additional embodiments

Disclosed herein are methods for characterizing a disease in a subject, the method comprising: obtaining a measurement of interest from a subject; selecting a target solution profile from a plurality of target solution profiles; and applying a target solution profile to the obtained measurement of interest to characterize the disease of the subject, wherein the target solution profile comprises: a measurement definition defining one or more subject changes associated with a disease; a measurement instrument resource that transforms a measurement of interest captured according to a measurement definition into a dataset, the measurement instrument resource being device agnostic and thereby interchangeable between different target solution profiles, wherein the measurement instrument resource is validated; and an interpretation resource consistent with the measurement definition, the interpretation resource configured to interpret the dataset from the measurement instrument resource and characterize the disease of the subject.

In various embodiments, the target solution profiles are pre-validated by implementing one or more qualification protocols for establishing solution equivalencies between multiple target solution profiles.

Further disclosed herein is a method for constructing a target solution profile characterizing a disease, the method comprising: generating a measurement definition of the target solution profile, the measurement definition defining one or more subject changes associated with the disease; selecting a measurement instrument resource that transforms data captured according to a measurement definition into a dataset, the measurement instrument resource being device agnostic and thereby interchangeable between different target solution profiles; and generating a target solution profile interpretation resource consistent with the measurement definition, the interpretation resource configured to interpret the data set from the measurement instrument resource and characterize the disease. In various embodiments, the methods disclosed herein further include implementing a qualification protocol for establishing solution equivalencies between the plurality of target solution profiles to validate the target solution profiles. In various embodiments, the measurement definition and interpretation resources are fixed for the target solution profile and specific to a disease. In various embodiments, the measurement instrument resources are interchangeable between different target solution profiles for characterizing different diseases. In various embodiments, the measurement instrument resource is specific to a class of devices. In various embodiments, such devices include wearable devices (e.g., wrist-worn devices), wearable devices, image and voice-based devices, non-contact sensors, and sensor-based smart devices (e.g., balances, thermometers, and respirators). In various embodiments, the measurement instrument resources include a machine learning model that transforms data captured according to measurement definitions into a dataset.

In various embodiments, a qualification protocol is implemented to verify solution equivalency between different classes of devices. In various embodiments, the target solution profile represents a standardized digital measurement solution for characterizing a disease. In various embodiments, the target solution profile includes a measurement stack that includes a plurality of layers. In various embodiments, each of the measurement definition, the measurement instrument resource, and the interpretation resource is represented by one or more layers of the plurality of layers. In various embodiments, the order of the plurality of layers includes one or more measurement definition layers, one or more measurement instrument resource layers, and one or more interpretation resource layers.

In various embodiments, the measurement definition layer interfaces with the measurement instrument resource layer, and the measurement instrument resource layer interfaces with the interpretation resource. In various embodiments, the one or more measurement definition layers include one or more of disease hypotheses and measurable concepts of interest. In various embodiments, the one or more measurement instrument resource layers include one or more of measurement methods, health data, and machine learning algorithms. In various embodiments, the one or more interpretation resource layers include one or more of analytical verification and clinical interpretation. In various embodiments, the one or more measurement definition layers include disease hypotheses and measurable concepts of interest, wherein the one or more measurement instrument resource layers include measurement methods, health data, and machine learning algorithms, and wherein the one or more interpretation resource layers include analytical verification and clinical interpretation.

In various embodiments, the disease is dementia. In various embodiments, it is hypothesized that intervention means to slow the progression of dementia are included. In various embodiments, the measurable concepts of interest include one or more of attention, language, or executive function. In various embodiments, the measurement method includes a method for capturing speech. In various embodiments, the health data comprises raw voice data captured from a subject, or magnetic resonance imaging data. In various embodiments, the machine learning algorithm includes one or more of a natural language processing algorithm, a clustering algorithm, and a clinical variable predictor. In various embodiments, the analytical verification includes evidence verification of machine learning algorithm performance. In various embodiments, the clinical interpretation includes evidence supporting disease characterization according to clinical dementia ratings.

In various embodiments, the disease is parkinson's disease. In various embodiments, it is hypothesized that interventions to reduce tremors are included. In various embodiments, the measurable concept of interest includes the ability to perform moderate-intensity daily activities. In various embodiments, the measurement method includes a method of capturing physiological data using a biosensor. In various embodiments, the health data includes raw physiological data captured using a biosensor. In various embodiments, the machine learning algorithm transforms the health data into a dataset. In various embodiments, the analytical verification includes evidence verification of machine learning algorithm performance. In various embodiments, the clinical interpretation includes evidence that supports disease characterization in a population of parkinson's disease patients.

Further disclosed herein is a method for providing one or more target solution profiles useful for characterizing one or more diseases, the method comprising: providing a directory comprising a plurality of target solution profiles, wherein each of the one or more target solution profiles comprises: a measurement definition of a target solution profile defining one or more subject changes associated with one of the one or more diseases; a measurement instrument resource that transforms data captured according to a measurement definition into a dataset, the measurement instrument resource being device agnostic and thereby interchangeable between different target solution profiles, wherein the measurement instrument resource is verified by implementing one or more qualification protocols for establishing solution equivalencies between a plurality of measurement instrument resources; and an interpretation resource consistent with the measurement definition, the interpretation resource configured to interpret the dataset from the interchangeable measurement instrument resource and characterize the disease of the subject. Receiving a selection of one or more target solution profiles from a third party; the selected one or more target solution profiles are provided to a third party.

In various embodiments, the target solution profile includes a measurement stack that includes a plurality of layers. In various embodiments, each of the measurement definition, the measurement instrument resource, and the interpretation resource is represented by one or more layers of the plurality of layers. In various embodiments, the order of the plurality of layers includes one or more measurement definition layers, one or more measurement instrument resource layers, and one or more interpretation resource layers. In various embodiments, the measurement definition layer interfaces with the measurement instrument resource layer, and the measurement instrument resource layer interfaces with the interpretation resource. In various embodiments, the one or more measurement definition layers include one or more of disease hypotheses and measurable concepts of interest. In various embodiments, the one or more measurement instrument resource layers include one or more of measurement methods, health data, and machine learning algorithms. In various embodiments, the one or more interpretation resource layers include one or more of analytical verification and clinical interpretation. In various embodiments, the one or more measurement definition layers include disease hypotheses and measurable concepts of interest, wherein the one or more measurement instrument resource layers include measurement methods, health data, and machine learning algorithms, and wherein the one or more interpretation resource layers include analytical verification and clinical interpretation.

In various embodiments, the methods disclosed herein further comprise: receiving a search query from a third party; for each of one or more target solution profiles of a plurality of target solution profiles, evaluating the target solution profile to determine whether the target solution profile satisfies a query; and returning a list of target solution profiles that satisfy the query. In various embodiments, evaluating the target solution profile includes: one or more measurement definition layers that satisfy the concept of interest of the query are evaluated. In various embodiments, the methods disclosed herein further comprise: in response to a request from a third party, replacing the instrumentation resources of the target solution profile with the second instrumentation resources to generate a revised target solution profile; and providing the revised target solution configuration file in a directory comprising a plurality of target solution configuration files.

Additionally disclosed herein is a non-transitory computer-readable medium comprising instructions that, when executed by a processor, cause the processor to: obtaining a measurement of interest from a subject; selecting a target solution profile from a plurality of target solution profiles; and applying a target solution profile to the obtained measurement of interest to characterize the disease of the subject, wherein the target solution profile comprises: a measurement definition defining one or more subject changes associated with a disease; a measurement instrument resource that transforms a measurement of interest captured according to a measurement definition into a dataset, the measurement instrument resource being device agnostic and thereby interchangeable between different target solution profiles, wherein the measurement instrument resource is validated; and an interpretation resource consistent with the measurement definition, the interpretation resource configured to interpret the dataset from the measurement instrument resource and characterize the disease of the subject.

Additionally disclosed herein is a non-transitory computer-readable medium for constructing a target solution profile characterizing a disease, the non-transitory computer-readable medium comprising instructions that, when executed by a processor, cause the processor to: generating a measurement definition of the target solution profile, the measurement definition defining one or more subject changes associated with the disease; selecting a measurement instrument resource that transforms data captured according to a measurement definition into a dataset, the measurement instrument resource being device agnostic and thereby interchangeable between different target solution profiles; and generating a target solution profile interpretation resource consistent with the measurement definition, the interpretation resource configured to interpret the data set from the measurement instrument resource and characterize the disease.

In various embodiments, the non-transitory computer-readable medium further includes instructions that, when executed by the processor, cause the processor to implement a qualification protocol for verifying the target solution profile, the qualification protocol for establishing solution equivalency between the plurality of target solution profiles. In various embodiments, the measurement definition and interpretation resources are fixed for the target solution profile and specific to a disease. In various embodiments, the measurement instrument resources are interchangeable between different target solution profiles for characterizing different diseases. In various embodiments, the measurement instrument resource is specific to a class of devices. In various embodiments, such devices include wearable devices, image and voice based devices, contactless sensors, and sensor based smart devices (e.g., balances, thermometers, and respirators). In various embodiments, the measurement instrument resources include a machine learning model that transforms data captured according to measurement definitions into a dataset.

Additionally disclosed herein is a non-transitory computer-readable medium for providing one or more target solution profiles useful for characterizing one or more diseases, the non-transitory computer-readable medium comprising instructions that, when executed by a processor, cause the processor to: providing a directory comprising a plurality of target solution profiles, wherein each of the one or more target solution profiles comprises: a measurement definition of a target solution profile defining one or more subject changes associated with one of the one or more diseases; a measurement instrument resource that transforms data captured according to a measurement definition into a dataset, the measurement instrument resource being device agnostic and thereby interchangeable between different target solution profiles, wherein the measurement instrument resource is verified by implementing one or more qualification protocols for establishing solution equivalencies between a plurality of measurement instrument resources; and an interpretation resource consistent with the measurement definition, the interpretation resource configured to interpret the dataset from the interchangeable measurement instrument resource and characterize the disease of the subject. Receiving a selection of one or more target solution profiles from a third party; the selected one or more target solution profiles are provided to a third party. In various embodiments, the target solution profile includes a measurement stack that includes a plurality of layers. In various embodiments, each of the measurement definition, the measurement instrument resource, and the interpretation resource is represented by one or more layers of the plurality of layers. In various embodiments, the order of the plurality of layers includes one or more measurement definition layers, one or more measurement instrument resource layers, and one or more interpretation resource layers. In various embodiments, the measurement definition layer interfaces with the measurement instrument resource layer, and the measurement instrument resource layer interfaces with the interpretation resource. In various embodiments, the one or more measurement definition layers include one or more of disease hypotheses and measurable concepts of interest. In various embodiments, the one or more measurement instrument resource layers include one or more of measurement methods, health data, and machine learning algorithms. In various embodiments, the one or more interpretation resource layers include one or more of analytical verification and clinical interpretation. In various embodiments, the one or more measurement definition layers include disease hypotheses and measurable concepts of interest, wherein the one or more measurement instrument resource layers include measurement methods, health data, and machine learning algorithms, and wherein the one or more interpretation resource layers include analytical verification and clinical interpretation.

In various embodiments, the non-transitory computer-readable medium further includes instructions that, when executed by the processor, cause the processor to: receiving a search query from a third party; for each of one or more target solution profiles of a plurality of target solution profiles, evaluating the target solution profile to determine whether the target solution profile satisfies a query; and returning a list of target solution profiles that satisfy the query. In various embodiments, the instructions that cause the processor to evaluate the target solution profile further comprise instructions that, when executed by the processor, cause the processor to: one or more measurement definition layers that satisfy the concept of interest of the query are evaluated. In various embodiments, the non-transitory computer-readable medium further includes instructions that, when executed by the processor, cause the processor to: in response to a request from a third party, replacing the instrumentation resources of the target solution profile with the second instrumentation resources to generate a revised target solution profile; and providing the revised target solution configuration file in a directory comprising a plurality of target solution configuration files.

Examples

Example 1: target solution profile and digital measurement solution for atopic dermatitis

The measurement stack is divided into nine separate layers. Each element includes unique information that can exist independently and provide additional value in combination with other elements. These elements have been categorized into three sub-stacks (otherwise referred to as "resources"): define a sub-stack, measure instrument sub-stack, and verify sub-stack. Defining a sub-stack describes a condition, meaningful health aspects, and concepts of interest. The regulatory acceptance of this sub-stack is valuable for conducting research and should be adjusted as early as possible. The measuring instrument sub-stack includes elements for collecting, analyzing and interpreting data. These layers include both specific solutions and the generally described solution categories. Finally, the validation sub-stack describes validation studies and regulatory approval to complete these solutions. Although a stack may be built starting from each element, the process typically begins with available measuring instruments or medical conditions. Atopic Dermatitis (AD) is shown here as traversing the measurement stack and example target solution profiles.

Fig. 5A depicts an example target solution profile for atopic dermatitis. The disorder describes a medical condition of a patient population. A meaningful health aspect (MAH) defines an aspect of the disorder for which patients expect: (1) not intended to be worse, (2) intended to be improved, or (3) intended to be prevented. Here, the condition is atopic dermatitis and the meaningful health aspect is night itching. Reference is made to a hypothetical element (e.g., element 1 shown in fig. 5A) that describes a meaningful health aspect goal. For example, here, it is assumed that nocturnal itching is reduced by drug X in the adult population suffering from moderate to severe AD.

The element 2 in the measurement stack refers to the concept of interest. Here, the concept of interest (COI) describes how this particular meaningful health aspect is to be measured. For example, if the goal is to measure nocturnal scratching, the concept of interest may be any of a total sleep time measure, an hourly scratching event, or a total scratching event. The COI is actually measured using the result to be measured (OTM). For example, the results to be measured include specific, measurable characteristics of the MAH described by COI for the assessment of the condition. Thus, the results to be measured reveal whether treatment for MAH is beneficial. For example, scratching events decrease after X weeks. Although not explicitly shown in fig. 5A, a single disorder may have multiple MAHs; one MAH may have multiple COIs; one COI may have a plurality of results to be measured.

The element 3 in the measurement stack refers to the measurement method, which is part of the resources of the measuring instrument. The measurement method includes hardware, software or firmware solutions. Examples are wearable devices, mobile applications and sensors. Furthermore, the complete software solution may be a measurement method (e.g. a voice battery) as long as the method is able to reliably measure OTM. As a specific example, the measurement method is a wristwatch with a three-axis MEMS accelerometer (10 Hz to 100 Hz). The device captures acceleration 24 hours a day, 7 days a week, for 7 days to 45 days (depending on the frequency set).

The element 4 in the measurement stack refers to the raw data. The element represents the raw dataset as a result of the measurement method. Generally, raw data has not yet provided meaningful health data. However, the raw data is a separate layer, as each of these data sets may be utilized by a variety of algorithms for different purposes. Here, the raw data layer is introduced as a raw file providing for example an acceleration of 10Hz to 100 Hz. This data was captured in 3D SI units (XYZ gravity) and 28 days of data was collected continuously. Briefly, this example describes what raw data (acceleration in 3D SI units), its frequency (10 Hz to 100 Hz), and the amount of data captured (28 days).

The element 5 in the measurement stack is referred to as an algorithm. The algorithm represents a method of transforming raw data into a meaningful healthy dataset. Thus, meaningful health datasets are easy to analyze and interpret. The algorithms may be integrated into the complete measuring instrument or the original dataset(s) may be transformed with the respective algorithm. For example, the philips resotronics rada algorithm interprets measurement device data as sleep and scratch events.

The elements 6 in the measurement stack are referred to as health data. The health data element describes a health data set generated by an algorithm from an initial raw data set. The health dataset may be evaluated as a separate dataset for multiple purposes. Thus, the health dataset is considered an independent resource and is included in the stack as a separate layer. For example, a meaningful health dataset provides total sleep time, scratching events per hour, and total number of scratching events.

The elements 7 in the measurement stack are referred to as technical and analytical verification. In addition, the element 8 in the measurement stack refers to clinical verification. The different verifications ensure that the digital metric is valid and thus can be identified as meeting the conditions approved by the clinical trial. At the sample level, the instrument output was evaluated both on the computer and in vitro. In particular, the technical verification of the digital resources (referred to as V1 in fig. 5A) ensures that the measuring instrument captures the basic analog data accordingly. Technical verification verifies whether the captured data results in the generation of appropriate output data. For example, technical verification ensures that the measurement instrument matches specifications (e.g., battery life, data storage capacity, and available measurement frequency) of the device used to capture the data.

Analytical verification (referred to as V2 in fig. 5A) the complete measuring instrument is usually evaluated in vitro. Analytical verification includes verification of device details, processing algorithms, and reliable health data output. For example, analytical verification verifies the ability to measure, detect and predict physiological or behavioral indicators based on appropriate measurement criteria. The specific example in fig. 5A shows an analytical verification of measurement properties including reliability, specificity and sensitivity established between the measuring instrument and the night scratch. Comparison was made with 8 nights of reference measurement data to verify ICC >85% (n=45). The ICC verifies whether the novel digital measuring instrument is superior to or at least comparable to the reference measurement (e.g. monitoring the infrared visual results of night-time scratch).

Clinical validation (referred to as V3 in fig. 5A) evaluates whether the solution identifies, measures, and predicts a meaningful clinical, biological, physical, functional state, or experience in a specific use environment (COU). This is stated by its study design. Clinical validation is in vivo validation, including specific target populations and study-specific digital endpoints. Clinical validation allows meaningful interpretation of the results and evaluates whether the solution is effective for answering clinical questions related to the measuring instrument sub-stack and the definition sub-stack. For example, clinical validation evaluates the effect of treatment on nocturnal scratching, correlation with other measures of disease (e.g., PRO, biomarkers), and the ability to measure changes in individuals.

The element 9 of the measurement stack is referred to as regulatory verification. Here, regulatory qualification of health authorities is important for accepting and employing digital metrics. All layers of the supervision validation evaluation measurement stack are mainly applied to definition elements and validation elements. For example, define, measure internal and external regulatory priorities of instrumentation and evidence.

As shown in fig. 5A, the target solution profile for the adult population of atopic dermatitis included a night scratch endpoint based on a somatorecorder. TSP represents a generic, solution agnostic stack. Here, all layers of the measuring instrument resource are generally described. The measurement method is a three-axis wrist-worn accelerometer with a range of specifications. In addition, the raw data layer, algorithm layer, and health data layer are generally described in terms of a specific range (e.g., 14 to 56 days of data collection, rather than a specific number of days). Furthermore, COI is generally described as night-time scratching. Many DMS (such as the DMS shown in fig. 5B) belong to the class represented by the TSP.

Fig. 5B depicts an example Digital Measurement Solution (DMS) for atopic dermatitis. Here, the DMS is filled with a night scratch end point based on a body movement recorder. A meaningful health aspect is nocturnal itching, and a total of 6 different concepts of interest have been identified (e.g., total sleep time, post-sleep wake time, sleep efficiency, hourly scratching events, hourly scratching duration, and total scratching events). Each of the different concepts of interest may be selected for a particular DMS. Thus, here, 6 different concepts of interest correspond to 6 different DMS of atopic dermatitis.

The element 3 in the DMS (measurement method) identifies the specific device used to capture the raw data. Specifically, as shown in fig. 5B, the GENEActiv Original watch captures raw data. Furthermore, the element 5 (algorithm) in the DMS identifies the specific algorithm for transforming the raw data (element 4) into the health data (element 6). Here, the algorithm in the DMS is the Philips Respironics RADA algorithm, which interprets the device data as sleep and scratch events.

Fig. 5C depicts the resource interchangeability of different digital measurement solutions for atopic dermatitis. Here, the interchangeability of resources enables the rapid development of many digital measurement solutions that incorporate different resources. For example, the central measurement stack in fig. 5C refers to the DMS shown in fig. 5B. Geneactiginealwatch is included in element 3 (measurement method). However, other devices such as Apple Watch (e.g., apple Watch 6), fitbit, and Actigraph (e.g., actigraph GT9X Link) may alternatively be included in element 3 in place of GENEActivwatch. These additional DMS also belong to the class represented by TSP. Furthermore, the Philips Respironics RADA algorithm may be interchanged with other types of algorithms, such as the Koneska health algorithm or the Tudor-Locke2014 algorithm. Furthermore, certain concepts of interest (e.g., total sleep time) in the measurement stack may be interchanged with other concepts of interest (e.g., hourly scratching events or total scratching events).

Example 2: target solution profile and digital measurement solution for pulmonary arterial hypertension

Fig. 6A depicts an example target solution profile for pulmonary arterial hypertension. Here, a meaningful health aspect is the ability to conduct activities of daily living, with the corresponding assumption being that a particular therapeutic intervention (e.g., drug X) improves the ability to conduct physical activities. The concept of interest to measure is the ability to perform daily activities while being affected by pulmonary arterial hypertension.

In a measurement instrument resource, the measurement method generally identifies a wrist-worn device having a device specification. Here, the measurement method is device technology agnostic, neither specific devices nor specific device software are identified. The raw data elements include raw data files captured using a measurement method (e.g., a wrist-worn device). The algorithm component identifies an algorithm that transforms raw data files captured using the measurement method into meaningful health data. The health data element comprises a meaningful health data set transformed by the algorithm of the aforementioned element. As shown in fig. 6A, examples of meaningful health data of pulmonary arterial hypertension include measures of daily activity (such as number of steps, vacuuming activity, etc.).

The analysis verification element ensures that the meaningful health dataset is reliable, efficient and sensitive to the concept of interest. Clinical interpretation identified significant improvement in daily performance in patients with PAH following drug X-stem prognosis.

Fig. 6B depicts a first example digital measurement solution for pulmonary arterial hypertension. Further, fig. 6C depicts a second example digital measurement solution for pulmonary arterial hypertension. Here, the digital measurement solutions shown in fig. 6B and 6C are each a common category represented by the target solution profile shown in fig. 6A.

Referring to the DMS shown in fig. 6B, the measurement method element specifies a specific wrist-worn device (e.g., actiGraph GT9x Link). Thus, the ActiGraph GT9X Link device captures data represented in the original data file. The algorithm element transforms the raw data file into a meaningful health data set. As shown in fig. 6B, an ActiGraph deterministic algorithm (e.g., actionlife 6) is implemented as an algorithm that transforms an original data file into a meaningful healthy data set. In summary, the DMS shown in fig. 6B specifies a specific device of the measurement method element and a specific algorithm of the algorithm element, as compared to the TSP shown in fig. 6A.

Referring to the DMS shown in fig. 6C, the measurement method element designates a specific wrist-worn device (e.g., garcinvivofit 4) different from the device designated in fig. 6B. Thus, the GarminVivofit4 device captures the data represented in the original data file. The algorithm element transforms the raw data file into a meaningful health data set. As shown in fig. 6C, a machine learning algorithm is implemented that transforms the measurements captured by Garmin Vivofit4 into a meaningful health dataset. In summary, the DMS shown in fig. 6C specifies a specific device of the measurement method element and a specific algorithm of the algorithm element, as compared to the TSP shown in fig. 6A.

Each of the digital measurement solutions shown in fig. 6B and 6C is validated via a qualification protocol to ensure that the corresponding DMS generates similar results. Example qualification protocols include the following:

1) Device for wearing a DMS by a participant, and reference device for a DMS that has been previously successfully authenticated

2) Participant walks by X steps (e.g., 100 steps)

3) The total number of steps measured by the device of the DMS is compared with the total number of steps measured by the reference device. If the difference is less than the threshold number (e.g., 10%), the device of the DMS is successfully authenticated.

Fig. 6D depicts at least reuse of measurement instrument resources in a digital measurement solution. Here, fig. 6D shows the target instrumentation profile (e.g., concept of interest elements, instrumentation resources, and analysis verification elements) for each DMS shown in fig. 6B and 6C. Notably, the target meter profile for each DMS is interchangeable and viable for other meaningful health and medical aspects. Thus, the stack of resources that make up the target instrumentation profile can be readily utilized and incorporated into different DMS. As a specific example, digital measurement solutions are needed to measure parkinson's disease related concepts of interest. Thus, these target gauge profiles for pulmonary arterial hypertension disorders are reused for measuring parkinson's disease.

Example 3: target solution profile and digital measurement solution for parkinson's disease

Fig. 7 depicts an example digital measurement solution for parkinson's disease. Here, the DMS for parkinson's disease incorporates a target meter profile reused from the DMS of pulmonary arterial hypertension disorders. For example, the target measurement instrument profile (including elements 2 to 7) is identical to elements 2 to 7 of the DMS for pulmonary arterial hypertension.

As shown in FIG. 7, the DMS for Parkinson's disease identifies a particular wrist-worn device (e.g., actigraph GT9X Link) in the measurement method element. In addition, the DMS for parkinson's disease identifies a specific algorithm (e.g., actiGraph deterministic algorithm (e.g., actiGraph 6)), which transforms the raw data file captured by ActiGraph GT9X Link into a meaningful health data set. These elements are here identical to the corresponding elements of the DMS for pulmonary arterial hypertension shown in fig. 6B. In summary, even though the condition (parkinson's disease) and meaningful health aspects (reduced limb tremor) are different in the DMS shown in fig. 7 compared to the DMS shown in fig. 6B, the elements of the measuring instrument resources are the same.

Example 4: standardized solutions for improved regulatory acceptance

FIG. 8A depicts a high-level overview relating to collaborative effort for developing standardized solutions. Here, multiple parties participate in collaborative efforts to co-develop standardized solutions. These parties participate in standardized and structured methods, which results in reduced turnaround and development times. These standardized solutions undergo dynamic regulatory assessment by involving the regulators in the early stages of development. The developed solution is then submitted (e.g., to a customer).

Fig. 8B depicts an example flow involving parties for dynamic supervision assessment implementing a standardized solution. The flow starts in step 1, which involves a Digital Endpoint Ecosystem and Protocol (DEEP) directory. Here, additional digital metrics and/or digital solutions are developed, and in addition, these digital solutions may be subject to dynamic regulatory acceptance. Dynamic Regulatory Acceptance (DRA) involves initiating a DEEP task that involves multiple stakeholders cooperating together on a common task. Specifically, at step 3, these stakeholders collaborate to develop a digital concise manual that includes a digital solution. As shown in fig. 8B, stakeholders of the parties may provide various inputs to the concise manual creation process, such as clinical views, patient perspectives, and/or technical feasibility. At step 4, a concise manual is submitted for regulatory approval, such that the supervisor accesses the concise manual that includes the digital solution.

The interaction between the supervisor and the stakeholder may be as follows: at step 5, the supervisor logs in, browses the DEEP directory to further understand the digital solution with additional context, and looks at public questions and private background material from both sponsors with private questions. If desired, the supervisor may re-engage with the stakeholder to obtain additional clarity and information. The supervisor sees that patient and clinician inputs have been incorporated. However, the supervisor still has a question, for example, that for the severe form of the disease, a comprehensive assessment of the scratching activity seems to be important. Thus, the supervisor requires additional background about the patient's behavior, such as the patient's location of scratching, how to scratch, the number of hours to scratch, etc. The supervisor also wants to ask the patient with less severe disease and to know if their scratching activities are different. The stakeholder (e.g., patient representative) provides this feedback. Here, patients with severe forms of the disease are also scratched with both hands and even with both feet. Alternatively, patients with a lighter form of the disease report that their itching usually bursts in one area and they eventually only scratch this area.

The supervisor then wants to ask the patient with a severe form of the disease for a camera solution. Given their disease, they can tolerate the use of this solution for some time? Patients respond to the word "can" because their disease is already a heavy burden, so they would like to do so in order to help find a solution. However, they do represent a concern for doing so for a long time. The supervisor considers all of these inputs and then formulates their responses to these questions. They agree that scratching is indeed very meaningful and can therefore be used as a critical endpoint.

Regarding these two solutions envisaged, the supervisor sees both good applicability in different kinds of trials. Both sound reasonably, but require evidence of their performance in detecting scratching activity, and the supervisor also recommends developing evidence to better understand how much change in this measure occurs to produce meaningful benefits to the patient. In addition, the influence on sleep and the degree of next-day sleep should be studied.

Regarding private issues, regulators suggest that pharmaceutical company a (severe disease) consider using both of these envisioned solutions in their trials. The study may be designed with a camera viewing period and a wearable device. It can be investigated whether a wearable solution can be a suitable alternative to obtain more robust video measures. Thus, if the added value of the video solution can be better understood, better guidance can be provided in the future. An ideal solution would be to use both in the study of the severe form of the disease, balancing the scientific value with the burden on the patient.

For pharmaceutical company B, the supervisor foresees that a single wearable device solution can be sufficient to measure these isolated scratch incidents. However, the supervisor suggests that the company should also work to know how relevant the video is to the wearable measure and then make informed choices in their trial design. The supervisor advises the sponsor to seek more advice as more evidence is available, and then discusses the particular trial design again.

Thus, if the supervisor sees enough evidence, the supervisor provides a regulatory acceptance of the digital solution at step 6. At step 7, collaborative tasks involving multi-party stakeholders are completed. The regulatory feedback is consolidated and linked to the catalogs (MAH, COI and TSP). The stakeholders involved in this administration process now get explicit guidance about the next steps. Both solutions have utility for different purposes, and both sponsors have begun planning solution development tasks. Pharmaceutical company a wishes to invest in both options, which are of interest to pharmaceutical company B, but obviously wishes to develop wearable solutions preferentially.

Example 5: example development procedure for standardized solutions involving Multi-party stakeholders

Suppose that pharmaceutical company a and pharmaceutical company B have generated their respective measurement definitions for atopic dermatitis and are interested in measuring the number of nocturnal scratching events. The remaining problem is how to capture these measurements. Pharmaceutical company a wishes to develop the correct solution for measuring its endpoint. Thus, pharmaceutical company a accesses and searches the DEEP directory to identify existing solutions.

For example, pharmaceutical company a types "scratch" in the resource search box, which results in the discovery of the sensor device, algorithm, and related data set for that use case. Thus, pharmaceutical company a can find the building blocks needed for its solution.

Here, pharmaceutical company a may create a new task to find guardian services, which may assemble digital solutions and maintain. Pharmaceutical company a will fully subsidize this work, setting access rights to pharmaceutical company a that fully owns the solution, but granting operation permissions to the guardian for 3 years, while the guardian is responsible for maintaining documentation of the solution, including any component upgrades within the permissions' period.

The guardian compiles the solution from the elements in the catalog and connects the solution to the already established measurement definition. Pharmaceutical company a is now available with the solutions they need in clinical trials.

In addition, pharmaceutical company B can now see a solution that can be used to obtain permission from guardians (who are notified that DMS is available for TSPs they follow/subscribe to). The performance of the solution appears promising and the licensing conditions appear fair. Pharmaceutical company B also decides to license the solution from the guardian.

In summary, multi-party stakeholders may quickly adopt solutions by providing more efficient approaches via standardized solutions.

Watch (watch)

Table 1: exemplary Condition of measurement Stack

/>

Table 2: an example of a measurement stack has a meaningful health aspect (MAH). Some descriptions of example MAH are purposely left Empty space。

/>

Table 3: example concept of interest (COI) of the measurement stack. Some descriptions of example COIs are purposely left blank。

/>

Table 4: example Target Solution Profiles (TSPs). Some descriptions of example TSPs are purposely left blank。

/>

Table 5: example Digital Measurement Solutions (DMS). Some descriptions of the example DMS are purposely left blank 。

/>

Claims

1. A method for characterizing a disease in a subject, the method comprising:

obtaining a measurement of interest from the subject;

selecting a digital measurement solution from a plurality of digital measurement solutions, wherein the plurality of digital measurement solutions belong to a common category represented by a target solution profile; and

applying the selected digital measurement solution to the obtained measurement of interest to characterize the disease of the subject,

wherein the digital measurement solution comprises:

a measurement definition defining one or more concepts of interest related to the disease;

a measurement instrument resource that transforms the measurement of interest captured from the measurement definition into a dataset providing information for characterizing the disease, wherein the measurement instrument resource of the digital measurement solution is specific to a device used to capture the measurement of interest; and

optionally, an evidence resource for performing one or more verifications on the data set generated by the measurement instrument resource,

wherein the target solution profile does not change over time and enables efficient lifecycle management of the plurality of digital measurement solutions.

2. The method of claim 1, wherein the target solution profile represents a generalization of the plurality of digital measurement solutions, wherein a measurement instrument resource of the target solution profile is device technology agnostic.

3. The method of claim 1, wherein performing the one or more verifications comprises performing one or more of a technical, analytical, or clinical verification.

4. A method according to claim 3, wherein performing the technical verification comprises comparing the dataset generated by the measurement instrument resource with specifications of one or more devices used to capture the measurement of interest.

5. The method of claim 3, wherein performing the analytical verification comprises: determining any of reliability, specificity, or sensitivity indicators of the dataset; and

the reliability, specificity or sensitivity index is compared to a threshold.

6. The method of claim 3, wherein performing the clinical verification comprises:

assessing the effect of treatment on the measurement of interest for the disease.

7. The method of claim 1, wherein the digital measurement solutions are pre-validated by implementing one or more qualification protocols for establishing solution comparability between the digital measurement solutions of the target solution profile.

8. The method of claim 7, wherein the qualification protocol includes the steps of:

a) Recruiting an N member participant group;

b) Capturing measurements of interest throughout the N-member participant group according to specifications of the digital measurement solution;

c) Transforming the measurement of interest into a dataset according to the specification; and

d) The dataset is validated to determine whether the digital measurement solution implements a comparable solution to the target solution profile.

9. The method of claim 8, wherein validating the dataset comprises:

determining whether a characteristic of the dataset meets a threshold for the target solution profile; and

in response to determining that the characteristic of the dataset meets the threshold, the digital measurement solution is validated as achieving comparability of the solution.

10. The method of claim 9, wherein validating the dataset further comprises storing an indication of successful validation in metadata of the digital measurement solution in response to determining a comparability of the digital measurement solution implementation.

11. The method of claim 10, wherein the metadata of the digital measurement solution is stored in a directory accessible by a third party user for inspection.

12. The method of claim 8, wherein the specification of the digital measurement solution represents upgrade capability compared to a previous version of the digital measurement solution.

13. The method of claim 12, wherein the specification of the digital measurement solution represents upgrade capabilities included in a new distribution device for capturing the measurement of interest.

14. The method of claim 13, wherein the upgrade capability is one of an upgrade battery, an upgrade data store, an upgrade acquisition frequency, or an upgrade data collection algorithm.

15. The method of claim 1, wherein the common category of the plurality of digital measurement solutions represents a common method of measuring activity from an individual.

16. The method of claim 15, wherein the common method of measuring activity uses a class of devices that includes one or more of wearable devices, devices including accelerometers, devices including gyroscopes, ingestible devices, image and voice based devices, non-contact sensors, and sensor-based smart devices (e.g., balances, thermometers, and respirators).

17. The method of claim 1, wherein the measurement instrument resource comprises a machine learning algorithm that transforms data captured according to the measurement definition into the dataset.

18. A method for constructing a digital measurement solution for characterizing a disease, the method comprising:

generating a measurement definition of a target solution profile, the measurement definition defining one or more concepts of interest related to the disease;

generating or selecting a measurement instrument resource for the target solution profile, the measurement instrument resource configured to transform data captured according to the measurement definition into a dataset, the measurement instrument resource being device technology agnostic and thereby interchangeable between different target solution profiles;

generating an evidence resource of the target solution profile for performing one or more verifications on the data set generated by the measurement instrument resource;

generating a digital measurement solution by means of at least a device specifying the measurement instrument resource for a target solution profile, wherein the digital measurement solution belongs to a common category represented by the target solution profile,

Wherein the target solution profile does not change over time and thereby enables an efficient lifecycle management of the plurality of digital measurement solutions.