WO2015031542A1 - Systèmes et procédés de prédiction de maladie rare - Google Patents

Systèmes et procédés de prédiction de maladie rare Download PDF

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Publication number
WO2015031542A1
WO2015031542A1 PCT/US2014/053018 US2014053018W WO2015031542A1 WO 2015031542 A1 WO2015031542 A1 WO 2015031542A1 US 2014053018 W US2014053018 W US 2014053018W WO 2015031542 A1 WO2015031542 A1 WO 2015031542A1
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disease
symptoms
candidate
diseases
patient
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PCT/US2014/053018
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English (en)
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Hendrik Anton MEIJER
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Bioneur, Llc
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Priority to PCT/US2015/064564 priority Critical patent/WO2016094450A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • Various embodiments described herein relate generally to the field of health- related predictive analytics, and more particularly to correlating multiple symptoms with rare disease information and applying customized algorithms to generate confidence values for possible rare disease matches.
  • Rare diseases are often chronic, debilitating, life-threatening and with degenerative progression. They not only have an impact on the patients, but they also have an impact on their family, friends, their physicians and society. 80% of rare diseases are genetic in origin and thus are present throughout a person's life. 50%> of the people affected by rare diseases are children. 30% of children with a rare disease will not live to see their 5th birthday. A recent survey showed that it takes 7.6 years in the US and 5.6 years in the UK for a patient with a rare disease to be properly diagnosed.
  • Systems and methods are provided herein for a rare disease matching and prediction portal where physicians can select from a list of patient symptoms which are then matched with a list of rare diseases to produce candidate diseases, after which the candidate diseases are evaluated based on weighted lists of symptoms for each candidate disease to produce a confidence indicating a likelihood that a patient suffers from one of the rare disease.
  • Information curated from peer reviewed medical and scientific publications related to rare diseases are utilized to determine the weighted list of symptoms for each disease, and a customized algorithm is applied to determine the confidences from a list of candidate diseases.
  • the portal may provide a disease profile for each disease which includes additional tests, specialized facilities and experts, and possible treatments for the candidate diseases.
  • a method of rare disease prediction comprises the steps of: receiving a list of patient symptoms pertaining to a patient; matching the list of patient symptoms with one or more candidate diseases; producing a list of weighted symptoms for each candidate diseases, wherein each symptom is weighted for each candidate disease based on the relevance of the symptom to the disease; and generating a confidence for each candidate disease based on a comparison of the list of weighted symptoms with the list of patient symptoms.
  • a system for rare disease prediction comprises a receiving unit which receives a list of patient symptoms pertaining to a patient; a disease matching unit which matches the list of patient symptoms with one or more candidate diseases; a symptom weighting unit which produces a list of weighted symptoms for each candidate disease, wherein each symptom is individually weighted for each candidate disease based on the relevance of the symptom to the disease; and a confidence generating unit which generates a confidence for each candidate disease based on a comparison of the list of weighted symptoms with the list of patient symptoms.
  • FIG. 1 is a block diagram illustrating an exemplary system for predicting likelihoods of rare diseases in patients, according to one embodiment.
  • FIG. 2 is a block diagram illustrating an exemplary prediction server configured to predict the likelihoods of rare diseases in patients, according to one embodiment.
  • FIG. 3 is a flow diagram illustrating an exemplary method of predicting the likelihoods of rare diseases in patients, according to one embodiment.
  • FIG. 4 is a block diagram that illustrates an embodiment of a computer/server system upon which an embodiment of the inventive methodology may be implemented.
  • the systems and methods described herein provide a portal for predictive analytics relating to comprehensive lists of symptoms weighted for each disease in order to determine the likelihood that a patient may suffer from a rare disease.
  • the embodiments described herein enable physicians to query multiple symptoms that the patient presents with and receive a list of candidate diseases and confidences associated with each candidate disease. The symptoms of these rare diseases may be ranked and/or weighted according to its specificity with a rare disease.
  • the portal will process the selected symptoms and their disease-specific weights with a customized algorithm to generate a confidence for each candidate disease. The confidences are then displayed to the physician along with disease profile information including confirmatory diagnostic tests, specialized facilities and possible treatments.
  • the embodiments do not provide a diagnostic indication, but can be regarded a
  • hypothetico-deductive method in the sense that the suggested candidate diseases can be viewed as hypotheses that require further disease-specific testing to confirm a positive or negative diagnosis.
  • the beneficiaries of these systems include patients, physicians, policy makers and industry.
  • Physicians are provided with a tool that supports them in the process of diagnosing a patient with a rare treatable disease, faster than their current capability.
  • Biopharmaceutical companies that developed and market these orphan drugs will have the opportunity to serve many more patients around the world and meet their unmet medical needs faster and without having to make significant investments in local country specific infrastructure and the like. As a result, its revenue will grow faster. Policy makers benefit as they look for statistics around the number of patients with a disease in a region or world-wide.
  • the embodiments described herein will increase the number of and expedite access to subjects eligible to participate in clinical trials.
  • the objectives are to 1) increase the number of and access to na ' ive (previously untreated) patients, 2) reduce the time of accruing these patients, 3) reduce the overall time of all phases of clinical trials, 4) reduce the overall costs of clinical trials, 5) reduce the overall "time to market" for a candidate drug and treatment of patients suffering from a rare disease, and 6) generate ROI faster.
  • the method can be extended and adapted to rare diseases for which no treatment with an orphan drug is available but for which clinical trials are on-going or scheduled to be conducted. A patient predicted to have an identified rare disease might meet the inclusion criteria for these clinical trials.
  • Finding patients suffering a specific rare disease and that can participate in a clinical trial is a significant challenge for CROs (Clinical Research Organizations).
  • a similar portal may be developed to be used by the general public for rare disease prediction that uses lay medical terms and descriptions.
  • the portal can be developed for common diseases without modification using the same principles for rare disease prediction, only with a larger data set of symptoms, diseases, severities and prevalences.
  • FIG. 1 illustrates a block diagram of one embodiment of a system for predicting rare diseases.
  • a physician or health care provider will access the system from a remote device 102 at the physician's location, allowing the physician to take advantage of the system from anywhere.
  • the remote device 102 is connected with a prediction server 104, which is configured to receive a user input of a selection from a list of patient symptoms provided by the physician.
  • the list of patient symptoms may also include a level of severity of each symptom, as will be discussed further herein.
  • Databases connected with the prediction server 104 may be configured to each store different types of information needed to process the list of selected patient symptoms and match the patient symptoms with symptoms of rare diseases, apply weights to symptoms for each rare disease, identify disease profile information and suggest treatments.
  • the system is designed to avoid transmitting or storing any information identifying the patient in order to avoid any risks of violating patient privacy rights. Therefore, the user only selects symptoms from a list and follows the steps to obtain predicted outcomes without creating a patient profile or entering any personally identifiable information about the patient.
  • multiple databases are illustrated herein, the system could alternatively contain a single database to store all of the information, which would be divided into normalized database tables. The multiple databases described herein are provided for clarity in order to illustrate the different types of information stored in the system.
  • a symptoms and diseases database 106 which stores lists of diseases and symptoms and their associations with each other.
  • the symptoms may be associated with the diseases through various tables, such as a table of diseases with a list of symptoms known to exist for each disease.
  • the symptoms and diseases will each receive identifiers (IDs), such that relational tables can be configured to relate the symptom IDs with their corresponding disease IDs.
  • IDs identifiers
  • Another separate database or database table may contain synonyms for symptoms and diseases in order to more accurately match the selected symptoms and diseases.
  • a weighting database 108 may store a list of weights that are applied to each symptom for each particular disease, although some symptoms may not have a weight. The weights reflect the prevalence of a particular symptom in a particular disease.
  • a symptom which is the primary symptom in a rare disease and which is also exclusive only to that rare disease may receive a weight value of 1 from a scale of 0 to 1 , since the existence of that symptom is a definitive indication that the patient suffers from the associated rare disease.
  • a disease profile database 110 may also be utilized to store complete descriptions and other information about each rare disease, such as detailed descriptions of the symptoms, how they are presented, the progression of the disease, diagnostic tests that can confirm its existence and specialists and facilities equipped to handle the disease.
  • a treatments database 112 may store a list of treatments that are applicable to certain rare diseases, such as lists of orphan drugs. The treatments database 112 may also include information on the availability and approval of certain treatments in certain locations, the name and location of laboratories which can perform needed tests, the availability of clinical trials relating to the disease and information on the efficacy of the treatments, costs, etc. The system may therefore provide clinical trials with access to additional naive patients that would otherwise remain unidentified and undiagnosed.
  • the prediction server 104 may also be connected with one or more disease knowledge sources 114 found outside the main system which gather information curated from thousands of peer reviewed medical and scientific publications related to rare diseases on diseases and symptoms. This information may be incorporated into any one of the databases in order to improve the overall accuracy of the prediction. For example, information on new symptoms of certain rare diseases may be incorporated into the symptoms and diseases database 106, while information on the prevalence of a particular symptom to a particular rare disease may be incorporated into the weighting database 108 to adjust the weight of that particular symptom with respect to that particular rare disease. All of the information can be added to the diseases profile database 110 to improve the information available on each disease, and updates on new treatments or the results of new treatments may be added to the treatments database 112.
  • the prediction server may be configured with one or more processing elements to receive, analyze and display information relating to the symptoms and confidences for rare diseases.
  • FIG. 2 illustrates a block diagram of the prediction server 104 with a receiving unit 116 configured to receive the list of selected patient symptoms from the input device 102. The received list of selected patient symptoms is then forwarded to a disease matching unit 118 which matches the list of selected patient symptoms with the symptoms and diseases in the symptoms and diseases database 106 in order to produce a list of candidate diseases, as will be described further below. The list of candidate diseases is then forward to a confidence generating unit 120 which accesses the weighting database 108 to utilize the weights assigned to each symptom for each disease in order to determine a confidence for each candidate disease. Once the confidences are computed, the prediction server 104 may store the results in an attached database or forward the results back to the input device 102 for viewing by the physician.
  • the portal will provide for a consistent, accurate real-time prediction of the supported rare diseases by adopting a symptoms weighting algorithm.
  • the diagnosis will rely on a curated relational database of symptoms and diseases. Many rare diseases are diagnosed by a unique combination of a set of symptoms and varying levels of severity. Therefore, the physician selecting the list of symptoms for a particular patient may also select a level of severity for each symptom in order to improve the confidence of the prediction. It is possible that multiple diseases are diagnosed by a given set of symptoms, and it is also plausible that a given disease is diagnosed by multiple sets of symptoms. Hence, the relationship between symptoms and diseases is that of many-to-many.
  • a set could contain one or more symptoms with varying levels of severity, which could also be incorporated into the method of predicting disease. Some symptoms may need to be described in terms of the level of severity described in terms of categories (e.g., mild, medium or severe) or numbers (e.g., temperature, pressure, a range such as 1-10, etc.) or types (benign or malignant) or temporal (juvenile or not), etc. Each symptom within a set may therefore contribute disproportionately to the prediction of a disease. In one embodiment, the severity of a particular symptom may be utilized as a pre- weighting factor for a symptom.
  • FIG. 3 illustrates one embodiment of a method for predicting the likelihood of rare diseases.
  • the physician or user first selects a set of patient symptoms (and possible severities) at the portal, which is then received at the prediction server.
  • the physician may be interacting with the portal via a web-interface or through an application running on the physician's input device, such as a desktop or portable computing device.
  • an initial matching process is performed by matching the list of selected patient symptoms with the list of symptoms stored in the associated diseases and symptoms database. Any disease with symptoms that match the list of patient symptoms is selected and then identified as a candidate disease.
  • the remaining process may skip the prediction algorithm (described below in step 308) and the identified disease may be immediately output as the predicted disease at step 310.
  • the confidence value would effectively be 100%, as there would be no other candidate diseases with the same set of symptoms. If the list of candidate diseases does not produce an exact match ("N" at step 306), the process continues to the prediction algorithm in step 3086.
  • step 308 the candidate diseases identified based on the initial matching are input into a prediction algorithm.
  • the prediction algorithm is applied to each candidate disease using the selected symptoms that are weighted for each candidate disease (obtained from the weighting database 108) to determine a confidence for each candidate disease that indicates a likelihood that the patient suffers from that particular disease.
  • the predictive algorithm sums the total weights for all of the identified symptoms, raises them by a value corresponding to the number of symptoms of the candidate disease not identified in the list of patient symptoms, and then subtracts them by a constant.
  • the predictive algorithm may therefore be understood by the following equation:
  • Cd is the confidence value
  • w is the weight w for a symptom
  • i is the number of identified patient symptoms
  • D is the number of symptoms not identified from a list of all symptoms for a candidate disease. Details of the weighting algorithm will be provided further below.
  • step 310 the predicted diseases and confidences are output to the physician.
  • the confidences may be output in order of the most likely disease to the least likely disease, and the number of candidate diseases displayed may be truncated to only provide the top five predictions or only the predictions over a certain threshold confidence value.
  • the confidences are represented herein as numerical values indicating a percentage from 0 - 100 for ease of understanding. However, the confidence may be represented as another numerical value on a different scale, or even converted to a descriptive term indicating whether it is "likely,” "very likely” or “unlikely” that the patient is suffering from the candidate disease.
  • the portal may display a user interface with a disease profile for each candidate disease, as has been described above.
  • the disease profile may provide detailed explanations of the disease, symptoms, timeline of onset and progression, etc. that is stored in the disease profile database 110.
  • the user interface may then display, in step 314, the disease profile information, including possible tests that a physician may prescribe to make a definitive diagnosis of the rare disease, possible treatments for the disease (such as a list of orphan drugs that are not typically known to the medical community), clinical trials available for potential new treatments and treatment centers or specialized medical centers that specialize in the diagnosis and treatment of a rare disease.
  • a disease profile may be sponsored by one of the treatment providers, clinical trial sponsors, treatment locations or diagnostic test providers.
  • the weighting algorithm is a function of five factors:
  • Optional Symptoms The number of optional symptoms. Presence of optional symptoms in addition to the presence of all mandatory symptoms will enhance the confidence assigned to overall predicted diagnosis. 3. Severity of Symptoms: The severity of symptoms. Mild occurrence of a symptom may reduce the weight assigned to it whereas severe occurrence would enhance it.
  • Combination of Symptoms Combination (subset) of symptoms collectively associated with the disease.
  • Likelihood Ratio Likelihood ratio of association of a symptom (or a set of symptoms) with a disease. This information may have to be mined from public health or rare disease databases such as those hosted by Orphanet, BiomedExperts, WebMD Symptoms, NIH, and NORD (rarediseases.org). For example, a certain symptom may or may not always be associated with a specific disease. This could be represented in terms of a ratio of the number of disease cases reporting this symptom (for e.g., in a database) to the total number of known cases with this disease. Since the numerator would be less than or equal to the denominator, the range of valid values for the likelihood ratio would be between 0.0 and 1.0.
  • a scoring and selection algorithm is used to identify candidate diseases, generate a score for the candidate diseases and determine whether the candidate diseases exceed a threshold.
  • the score for a disease is the weighted count of the intersection between the mandatory symptoms for the disease and the provided symptoms, plus the weighted count of the intersection between the optional symptoms for the disease and the provided symptoms.
  • Equation 3 the equation for determining the score of a diseases is as follows in Equation 3 :
  • Score(D, ⁇ S ⁇ ) (Count( ⁇ S m (D) ⁇ ⁇ (S ⁇ )*W m ) + (Count( ⁇ S 0 (D) ⁇ ⁇ (S ⁇ )*W 0 ) (3), where:
  • W m Weighting factor for mandatory symptoms
  • W 0 Weighting factor for optional symptoms
  • the visibility of a disease in the result set is a Boolean operation based on whether the score for the disease is greater than a pre-computed threshold value.
  • the visibility is determined by Equation 4:
  • Visibility(D, ⁇ S ⁇ ) If(Score(D, ⁇ S ⁇ ) > Threshold) then true else false (4).
  • the visibility threshold (Threshold) is determined by a bi-state function which evaluates to a pre-set value, T, if any disease's score is greater than or equal to T for the set of all possible diseases, or evaluates to 0 if not.
  • the Threshold may be determined, in one embodiment, by Equation 5 :
  • Threshold If(Max(Score( ⁇ D ⁇ , ⁇ S ⁇ )) ⁇ T) then 0 else T (5), where:
  • T Threshold score for disease inclusion
  • Count The number of items in a set.
  • T 20.
  • the above diseases scoring and selection algorithm provides an additional stepwise process to determine candidate diseases.
  • ICD www.who.int/classifications/icd/en/
  • OMIM Online Mendelian Inheritance in Man (www.omim.org/)
  • Web-based software quality requirements that will be adhered to will include: 1) the portal will be developed using a strict Agile software development methodology at capability maturity model (CMM) Level 3 maturity; 2) 508 compliance: www.section508.gov/; and 3) W3C Web Accessibility Initiative to promote a high degree of usability for people with disabilities.
  • CMS capability maturity model
  • the portal will comply with all applicable and evolving regulatory requirements set- forth by FDA, EM A and other international and national agencies.
  • the system includes a scroll down menu with the names of all
  • This portal will include the names of national and international clinical rare disease experts and their centers of excellence. This portal will include information about local, regional and international patient societies and advocacy groups.
  • the system may also provide an interface and content for patients and related parties separate from the physician interface.
  • a similar portal would be available for use by patients and their families. This portal will describe the same 400 or more rare diseases, symptoms and treatment(s) but in lay terms only. Everything, including the query of multiple symptoms will be lay term based. This portal would be available in all major languages. With the knowledge obtained patient(s) can visit their physician and have him/her use the portal for further research.
  • the patient interface may also provide for interactions between patients suffering from similar rare disease conditions so they can discuss the treatments, symptoms and overall experiences.
  • the information received from the physicians regarding the symptoms and severities may be utilized along with the predicted confidences as additional analytics helpful to further improve the accuracy of identifying rare diseases and their symptoms.
  • the system does not collect information that would identify a specific individual patient in order to protect privacy, the information collected would be limited to a collection of symptoms, severities and predicted diseases associated with an anonymous patient.
  • information on the location of the physician inputting the patient data could be used in order to track the locations of cases of rare disease.
  • the system may also be configured to continuously receive third party information from publications, papers, hospitals or other healthcare facilities, etc. that collect patient data.
  • FIG. 4 is a block diagram that illustrates an embodiment of a computer/server system 400 upon which an embodiment of the inventive methodology may be implemented.
  • the system 400 includes a computer/server platform 401 including a processor 402 and memory 403 which operate to execute instructions, as known to one of skill in the art.
  • the term "computer- readable storage medium” as used herein refers to any tangible medium, such as a disk or semiconductor memory, that participates in providing instructions to processor 402 for execution.
  • the computer platform 401 receives input from a plurality of input devices 404, such as a keyboard, mouse, touch device or verbal command.
  • the computer platform 401 may additionally be connected to a removable storage device 405, such as a portable hard drive, optical media (CD or DVD), disk media or any other tangible medium from which a computer can read executable code, data or write results to.
  • the computer platform may further be connected to network resources 406 which connect to the Internet or other components of a local public or private network.
  • the network resources 406 may provide instructions and data to the computer platform from a remote location on a network 407.
  • the connections to the network resources 406 may be via wireless protocols, such as the 802.11 standards, Bluetooth® or cellular protocols, or via physical transmission media, such as cables or fiber optics.
  • the network resources may include storage devices for storing data and executable instructions at a location separate from the computer platform 401.
  • the computer interacts with a display 408 to output data and other information to a user, as well as to request additional instructions and input from the user.
  • the display 408 may therefore further act as an input device 404 for interacting with a user.

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Abstract

L'invention concerne un portail d'appariement et de prédiction de maladie rare dans lequel des médecins peuvent sélectionner un certain nombre de symptômes qui sont alors appariés à une liste de maladies rares pour produire des maladies candidates, après quoi les maladies candidates sont évaluées sur la base de listes pondérées de symptômes pour chaque maladie candidate pour produire un indice de confiance indiquant une probabilité qu'un patient souffre d'une des maladies rares. Des informations gérées provenant de publications médicales et scientifiques revues par des pairs et d'autres bases de données gérées associées à des maladies rares sont utilisées pour déterminer la liste pondérée de symptômes pour chaque maladie sur la base de la prévalence d'un symptôme pour une maladie, et un algorithme personnalisé est appliqué pour déterminer les indices de confiance à partir d'une liste de maladies candidates. En même temps que indices de confiance, le portail peut fournir un profil de maladie pour chaque maladie qui inclut des tests additionnels, des établissements et des experts spécialisés, et des traitements possibles pour les maladies candidates.
PCT/US2014/053018 2013-08-27 2014-08-27 Systèmes et procédés de prédiction de maladie rare WO2015031542A1 (fr)

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JP2019537717A (ja) * 2016-11-01 2019-12-26 ハイコア バイオメディカル エルエルシー 入力データに基づきアッセイを提案することのできる免疫アッセイシステム
EP3535584A4 (fr) * 2016-11-01 2020-05-06 Hycor Biomedical, LLC Système de dosage immunologique apte à suggérer des dosages sur la base de données d'entrée
JP7069151B2 (ja) 2016-11-01 2022-05-17 ハイコア バイオメディカル エルエルシー 入力データに基づきアッセイを提案することのできる免疫アッセイシステム
CN116403735A (zh) * 2023-06-05 2023-07-07 山东志诚普惠健康科技有限公司 一种云健康服务平台的数据交互系统及方法
CN116403735B (zh) * 2023-06-05 2023-08-11 山东志诚普惠健康科技有限公司 一种云健康服务平台的数据交互系统及方法

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