Classifications

• • 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
  • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• • 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
  • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

• the immune checkpoint inhibitors anti-PD-(L)l antibodies pembrolizumab, nivolumab and atezolimumab have been approved by the FDA and EMA as monotherapy regimens in the second-line NSCLC setting after progression on platinum -based chemotherapy (Herbst et al. 2016, Lancet. 387: 1540-1550; Borghaei et al., 2015, NEJM, 373:1627-1639; Rittmeyer et al. 2017, Lancet 389:255-265).
• These second-line studies highlighted two key observations.
• the system supports the following activities for patient monitoring and management by the medical personnel or user, including: quickly access important clinical data to help manage cancer conditions provides an engaging interface for clinicians or users to collect and display data longitudinally and across medical specialties offers a solution for Tumor Board meetings allowing to overview efficiently a patient medical case display multimodal clinical data and omics data in a combined way for a better understanding of the medical case the ability to build patient cohorts based on their full clinical profile and then infer the best follow-up strategy for a given patient (in terms of prognostic, diagnosis, and/or treatments) improve the clinical trial enrolment process with more clinical and omics data, qualitatively and quantitatively offer a complete platform and framework to Clinical Research Programs that enable such programs to process efficiently a large amount of patient data (clinical and omics data).
• Figure 1 illustrates an embodiment of the selection of a patient.
• Figure 14 illustrates a block diagram of an electronic device that can implement one or more aspects of an embodiment of the invention.
• first-line treatment refers to any treatment, such as cancer treatment intended to heal, delay or relive the symptoms of a disease.
• first-line treatment or “primary/initial treatment” or “induction therapy” refers to the initial, or first treatment recommended for a given disease, such as cancer.
• first-line treatment of stage IV NSCLC may be a pembrolizumab monotherapy, a chemotherapy and pembrolizumab combination therapy, a chemotherapy doublet, such as the combination of a platinum chemotherapy with gemcitabine, vinorelbine or a taxane, and any other suitable treatment.
• Stecond-line treatment is treatment for a given disease, such as cancer after the first-line treatment has failed, stopped working, or has side effects that aren't tolerated.
• the patient’s response to a treatment may be binary classified as (1) progression (a complete or partial progression) or (2) no progression (a stable disease or absence of progression).
• the patient’s response to a treatment may be classified as (1) a complete response, wherein all the symptoms disappear and there is no evidence of disease; (2) a partial response, wherein the symptoms declined by a percentage, but disease remains; (3) a stable disease wherein the symptoms and the disease don’t progress but are not decreasing or (4) progression, wherein the disease has further developed.
• the patient’s response to a cancer treatment may be classified as (1) a complete response, wherein all of the cancer or tumor disappears and there is no evidence of disease; (2) a partial response, wherein the cancer has shrunk by a percentage but disease remains; (3) a stable disease, wherein the cancer is neither shrinking nor growing (no change in cancer progression) or (4) progression, wherein there is a progression so that a cancer has further developed.
• the patient’s response to a treatment may be provided as a metric that reflects the probability of the patient’s response to the treatment.
• confidence ranges may be provided.
• PFS progression-Free Survival
• the term “Overall Survival (OS)” refers to the length of time which begins at diagnosis (or at the start of treatment) and up to the time of death.
• the PFS and OS are commonly referred to as survival endpoints and measure the efficacy of cancer treatments.
• the term “Duration of Response (DoR)” refers to the length of time from response (R) of cancer to a treatment (improvement) to the disease worsening again (progression/death). The DoR is commonly referred to as early efficacy endpoint.
• TTP Time-To-Progression
• RECIST monitoring refers to a set of published rules established by the European Organization for Research and Treatment of Cancer (EORTC), National Cancer Institute (NCI) of the United States and Canadian Cancer Trials Group (CCTG) and pharmaceutical companies. It defines when tumors in cancer patients improve (“respond”), stay the same (“stabilize”), or worsen ("progress") during treatment. It provides a simple and pragmatic methodology to evaluate the activity and efficacy of new cancer therapeutics in solid tumors, using validated and consistent criteria to assess changes in tumor burden.
• the RECIST specification establishes a minimum size for measurable lesions, limits the number of lesions to follow and standardizes unidimensional measures. Monitoring parameter, is a clinical parameter used to assess the evolution of the patient disease.
• clinical data are used in methods as described herein, wherein a computer-implemented system is configured to monitor disease, such as a patient with nonsmall cell lung cancer (NSCLC), particularly a patient with stage IV non-small cell lung cancer.
• NSCLC nonsmall cell lung cancer
• Clinical data for any patient may include, but is not limited to the patient's: - demographics such as gender, age (month and year of birth), ethnicity, height and weight;
• Clinical data for the patient with stage IV NSCLC diagnosis may include, but is not limited to the patient's:
• demographics such as gender, age (month and year of birth), ethnicity, height and weight;
• - medical history such as smoking status, personal history of autoimmune diseases, preexisting lung disease, previous familial history of cancer and previous personal history of cancer;
• stage IV NSCLC diagnosis and subtype IVA, IVB
• performance status at stage IV NSCLC diagnosis corticosteroids and antibiotics treatment history
• therapy received and number of cycles by progression and/or in total presence of treatment toxicity leading to treatment discontinuation, hospitalization and/or death and organs affected
• performance status and clinical response at first/further evaluation progression status, including date and site of progression, treatment status after progression and vital status at last available instance.
• Clinical data refers to information that may also comprise descriptive data such as patients’ response to the treatment status, patient’s disease progression.
• the descriptive data may be further categorized such as, for example, the patient’s response to the treatment may be classified as a complete response, a partial response, a stable disease or a progression, wherein the patient’s disease progression may be classified as an increased growth speed and invasiveness of the tumor cells. This classification may be assigned numerical variables at the pre-processing step.
• multimodal data or “multiomics data” refers to a set of at least two types of data selected from clinical, biological, genomic and radiological data.
• the clinical data can further comprise “biological data” for the patient and may include but is not limited to the patient's: disease type and stage, expression level of relevant disease genes, such as receptors, blood analysis at baseline and first/further evaluation (hematology and biochemistry).
• Blood analysis results may comprise red-blood cell count, white blood count and/or biochemistry.
• the blood analysis results comprise white blood cells count, neutrophils count, lymphocytes count, monocytes count, eosinophils count, basophils count, platelets count, red blood cells count, hemoglobin levels, LDH levels, albumin, CRP levels.
• Biological data for the cancer patient may include but is not limited to the patient's: cancer stage and histopathology type at diagnosis, expression level of relevant receptors, blood analysis at baseline and first/further evaluation (hematology and biochemistry).
• Biological data for the patient with stage IV NSCLC diagnosis may include but is not limited to the patient's stage IV NSCLC histopathology type at diagnosis, PD-L1 expression level, immunohistochemistry antibody used for PD-L1 measurement, blood analysis at baseline and first/further evaluation (hematology and biochemistry).
• the biological data for a patient with a lung cancer may comprise data on PD-L1 expression on tumor cells.
• the biological data for a patient with a lung cancer may comprise at least one or consist of the following: stage IV NSCLC histopathology at diagnosis, PD-L1 expression level, immunohistochemistry antibody used for PD-L1 measurement, date of blood analysis at baseline, white blood cells count at baseline, neutrophils count at baseline, lymphocytes count at baseline, monocytes count at baseline, eosinophils count at baseline, basophils count at baseline, platelets count at baseline, red blood cells count at baseline, hemoglobin levels at baseline, LDH levels at baseline, albumin levels at baseline, CRP levels at baseline, date of blood analysis at first/further evaluation, white blood cells count at first/further evaluation, neutrophils count at first/further evaluation, lymphocytes count at first/further evaluation, monocytes count at first/further evaluation, eosinophils count at first/further evaluation, basophils count at first/further evaluation, plate
• Biodata for the patient may include digital pathology data and proteomic data.
• Genomics data may include digital pathology data and proteomic data.
• genomic data refers to a digital representation of genomic information, such as a DNA sequence.
• genomic data may be viewed as including “molecular data”.
• genomic data may refer either to a raw nucleotide DNA sequence out from a sequencer (FASTQ file format), and/or to an aligned nucleotide sequence relative to a reference genome (BAM or SAM file format), and/or to a list of variants out from a variant calling step (VCF file format), and/or a list of annotated variants out of a variant annotation step (VCF file format).
• a “variant” or a “genomic variant” refers to genomic sequence differences relative to a reference sequence. In bioinformatics data processing, a variant is uniquely identified by its position along a chromosome (chr,pos) and its difference relative to a reference genome at this position (ref, alt). Variants may include single nucleotide permutations (SNPs) or other single nucleotide variants (SNVs), insertions or deletions (INDELs), copy number variants (CNVs), as well as large rearrangements, substitutions, duplications, translocations, and others.
• SNPs single nucleotide permutations
• SNVs single nucleotide variants
• INDELs insertions or deletions
• CNVs copy number variants
• a variant caller may apply variant calling to produce one or more variant calls listed in a Variant Calling File (VCF format).
• VCF format Variant Calling File
• a germline variant is a variant inherited from at least one individual parent that differs from the wildtype genomic value as registered in a reference database, and that is present in all normal cells of the individual.
• a somatic variant or a somatic mutation or a somatic alteration is a variant caused by a genomic alteration, that is present in one or more somatic cells of the individual, for example in tumor cells.
• a “mutation” or a “mutated gene” refers to a gene for each at least one variant has been identified.
• a “mutated gene status” may be classified as mutated in the latter case or normal otherwise. This status is routinely used as a biomarker in cancer diagnosis and prognosis. For instance, the ALK gene mutation or the EGFR gene mutation have been shown of particular relevance in relation with lung cancer.
• a “mutational load” or “mutation load” or “mutation burden” or “mutational burden”, or for a tumor a “tumor mutational burden” or “tumor mutational load” or “TMB” refers to a biomarker measured as the number of somatic mutations per megabase of an interrogated genomic sequence.
• a “MSI status” or “Microsatellite Instability status” or “Micro satellite instability status” refers to the status of a genomic alteration due to insertions or deletions of a few nucleotides in the microsatellite repeat regions based upon one nucleotide repeat (homopolymers) or a few nucleotides (heteropolymers), due a DNA mismatch repair system deficiency.
• This status is routinely used as a biomarker in cancer diagnosis and prognosis, and in particular in uterine, colon and stomach cancers such as UCES (Uterine Corpus Endometrial Carcinoma), COAD (Colon Adenocarcinoma) and STAD (Stomach adenocarcinoma).
• UCES User Corpus Endometrial Carcinoma
• COAD Cold Adenocarcinoma
• STAD Sty adenocarcinoma
• the MSI status of genomic alterations for a patient is usually categorized as:
• a “homologous recombination deficiency status” or “HRD status” refers to a classification of homologous recombination pathway and relates to any cellular state/event that results in homologous recombination pathway deficiency. HRD status may be classified as positive (HRD+) wherein a homologous recombination pathway is deficient or may be classified as negative (HRD-) wherein a homologous recombination pathway is not deficient or may be classified as undetermined otherwise (HRD uncertain, HRD unknown).
• HRD status may be classified as positive (HRD+) wherein a homologous recombination pathway is deficient or may be classified as negative (HRD-) wherein a homologous recombination pathway is not deficient or may be classified as undetermined otherwise (HRD uncertain, HRD unknown).
• a “genomic pathway” or a “genetic pathway” refers a set of genomic loci or gene expression data which are significantly impacted in
• Genomics data for the cancer patient may include but are not limited to the patient's cancer mutational status such as obtained through NGS VCF file.
• omics data modality that can provide insights into patient diagnostic status and clinical outcome can also be used in and integrated in the described system.
• a non-exhaustive list includes transcriptomics, epigenomics, metabolomics, metagenomics, pharmacogenomics, spatial genomics.
• the data input is further processed through a series of data processing layers to implicitly capture the hidden data structures, the data signatures and underlying patterns. Thanks to the use of multiple data processing layers, deep learning facilitates the generalization of automated data processing to a diversity of complex pattern detection and data analysis tasks.
• the machine learning model may be trained within a supervised, semi-supervised or unsupervised learning framework. Within a supervised learning framework, a model learns a function to map an output result from an input data set, based on example pairs of inputs and matching outputs.
• the step of imputing missing features is performed when the patient’s multimodal features are at least 60% complete, at least 65% complete, at least 70% complete, at least 75% complete, or preferably at least 75% complete. Percentage of completeness of data may be calculated as relative to the complete set of features that can be extracted for the data from the patient.
• the computer system of the invention displays a prediction of clinical outcome, wherein the prediction is complemented by a report with the list of informative features identifiers used for the prediction machine learning model training, or treatment features’ relative contribution or weights used in the method of predicting treatment response or efficacy of a patient.
• the prediction is made earliest at first evaluation time and the prediction of treatment response or treatment efficacy of a patient is for a subsequent evaluation time, such that the prediction may be made at second evaluation time for a third evaluation time, and the like combinations.
• the current treatment may be displayed on a second window under the title “Treatment Plan”.
• the second window displays details of the Treatment Plan and may comprise therapy type (e.g., surgery, radiotherapy, pharmaceutical therapy, other), medication used in case of pharmaceutical therapy, dosage or dose prescription as suitable, number of cycles/fractions received as suitable, start date, as suitable, end date as suitable, dates of all specific therapy events as suitable.
• therapy type e.g., surgery, radiotherapy, pharmaceutical therapy, other
• medication used in case of pharmaceutical therapy e.g., dosage or dose prescription as suitable
• number of cycles/fractions received as suitable e.g., start date, as suitable, end date as suitable, dates of all specific therapy events as suitable.
• a third window may be dedicated to the result of the imagery, for example, showing the evolution of a tumor.
• the second middle window may display the blood analysis result of the patient at a specific date (as indicated in the upper part of the window).
• a column shows what should be the normal range.
• each time that a value is outside the normal range the value of the patient is highlighted.
• the blood results comprise white blood cells count, neutrophils count, lymphocytes count, monocytes count, eosinophils count, basophils count, platelets count, red blood cells count, hemoglobin levels, LDH levels, albumin, CRP levels.
• the user may see blood results as measured at another time point by clicking at the clickable arrowhead next to the date. Such clicking may retrieve and display the blood results of the previous or next timepoint.
• a ‘cohort’ is defined by a set of criteria applied on the clinical data.
• the cohort is linked by a predefined criteria, or range of criteria, of at least one clinical parameter of a patient.
• a patient cohort can be defined by identification of patients with similar characteristics or based on a set of inclusion criteria.
• a cohort is defined by a set of cohorting clinical parameters for which the cohorting inclusion criteria has a specific value. Those criteria can include a diversity of data including patient demographic data (e.g. birth, death, entry or exit of a clinical study), diagnosis status, patient performance status, genomic, clinical, biological or radiomics features, toxicity, disease progression, risk factors or any monitoring parameter, histopathology type, or depend on a one or more thresholds defined based on patient data.
• Cohorting refers to the process of assembling a cohort based on a number of shared characteristics or a set of inclusion criteria. Cohorting may be performed automatically and for a given diagnostic status based on a set of pre-defined inclusion criteria. These criteria are applied on the clinical data of the patients and are referred as “cohorting inclusion criteria”. Alternatively, the user may define cohorting criteria based on available patient parameters. [0109] According to one embodiment, the computer system of the present disclosure may comprise a cohort database comprising a plurality of cohorts, each cohort being represented by a set of cohorting parameters selected among the clinical parameters, each cohorting parameter having a predefined cohorting inclusion criteria, said system being configured to execute the steps of :
• the process of creating a new cohort is a two steps process.
• the figure 7 illustrates this process.
• a first parsing process can take place on the patient’s database PDB.
• the patient database PDB contains the clinical data of the patients. As explained above, each patient is identified by a patient identifier.
• This step creates a list of patients part of the study group.
• the clinical data of the patients in the patient database PDB are compared with the study inclusion criteria of the study group to extract the list of patients matching these criteria. “Matching” means that the clinical data has the same value as the criteria or within the range defined by the criteria.
• the second step is to refine the criteria and add more criteria.
• a set of cohorting parameters are defined which include the first set of study parameters defining the study group.
• a set of cohorting inclusion criteria are defined as criteria to parse the patient’s database and to extract the list of patients part of the new cohort. Alternatively, the system can parse only the list of patients of the study group with the additional criteria defining the cohort.
• the system generates a list of patients included in the cohort and a list of patients not in the cohort but in the study group (see figure 7). The number of patients in these two lists are displayed and the comparison of these numbers indicates to the user the pertinence of the new cohort.
• the system also can display the clinical data of the patients included in the cohort and of the patients not in the cohort but in the study group.
• the set of cohorting parameters with the cohorting inclusion criteria are stored in the cohort database CDB.
• the cohort database stores also the current list of patients part of the cohort for future comparison.
• the list of patients is represented by the list of the patient’s identifier.
• the study group is filtered to have only the list of patients for which the clinical data of the cohorting clinical parameters is known. This way, the number of the study group is more accurate and the comparison between the number in the study group and the number of the patients in the cohort (called “cohort group”) is more accurate.
• the number of patients of study group and the number of patients in the cohort group are displayed and can be compared. Further information can be displayed such as the clinical data corresponding to the cohorting clinical parameters for the non-cohort group and the cohort group.
• a “non-cohort group” is defined by patients part of the study group but not in the cohort group. It is therefore possible to analyse and compare the clinical data of the non-cohort group with the clinical data of the cohort group.
• the system comprises processing means to compare and display, for the cohorting parameters, the clinical data of the patients within the new cohort with the clinical data of the study group. This comparison may be completed by the display of a comparison metric representing statistical significance of the difference between the clinical data of the patients within the cohort and the clinical data of the group of patients not part of the cohort.
• One method is to use the p-value representing the statistical significance of the difference between the clinical data of the patients within the new cohort and the clinical data of the study group (or the non-cohort group).
• a cohort database is connected to the system and comprises a list of patients part of the cohort. Each cohort may be linked by a set of cohort clinical parameters and inclusion criteria and the patients fitting into these criteria may be part of the cohort.
• the computer system may then be configured to determine a statistical representation of the cohort representing the proximity of the patient’s clinical data with the clinical data of the patients part of the cohort.
• a metric is thus produced for each cohort and the computer system may display on a window the result of the evaluation, showing, for example, through a gaussian representation, each cohort in X and the metric related to this cohort in Y.
• the application may support cohorting for a plurality of patients that belong to the user account (i.e., the medical institution or department of the institution of the user).
• the application may also support inclusion of a plurality of patients from other accounts (i.e., other medical institution or department of the institution). These patients may be further filtered to restrict inclusion for example to, certain hospitals or medical centers, clinical research consortiums or geographic locations.
• the list of patients in a cohort may be updated, automatically by the application, when new patient or new patient data is available and matches (or is included in) the set of inclusion criteria of any cohort.
• the system may support user notification when a patient automatically enters or exits any cohort. Further details will be given below.
• the application also supports cohorting in the context of a clinical study to follow patient inclusion and offers specific statistical analysis tools.
• the application may allow comparison of results for the cohort to results of other cohorts, including clinical outcome including comparison of clinical outcome and descriptive statistical analysis.
• the comparison may also include statistical comparison estimations such as p-value or hazard ratio.
• the application may allow monitoring of more than one cohort simultaneously to support patient management decisions for one or several patients.
• the application may be configured to support clinical decision making for patients with the same characteristics simultaneously. This can be used for managing patient groups within one institution or in the context of a clinical study, for example.
• the application further allows the user to follow up with these cohorts using cohort analysis tools specific to the study, for example creation of sub-cohorts based on a subset of cohorting parameters from the clinical study cohort and comparison of these sub-cohorts outcome and descriptive statistical analysis to the other patients of the study.
• the creation of a sub-cohort from a main cohort is based on using the same cohort parameters and inclusion criteria of the main cohort with at least one additional parameter, thus narrowing the criteria and reducing the number of patients in the sub-cohort.
• the system may offer the possibility to link a particular cohort set of parameters to therapeutic guidelines so that the guideline or clinical recommendation is automatically proposed to the clinician when a patient clinical data matches (or is included in) the cohort inclusion criteria.
• the application also may allow the user to compare the characteristics of a patient or a plurality of patients with therapeutic guidelines, for example, those provided by the National Comprehensive Cancer Network to identify patients that are eligible for a particular treatment.
• the user may compare patient and cohort information for each of the guideline criteria and anticipate potential clinical management follow up decisions.
• Cohorts may either be predefined and stored or be assembled de novo based on the stored cohorting parameters.
• the system supports assembling cohorts using data for a plurality of patients linked to the user account.
• the system may also support inclusion of a plurality of patient clinical data available from other accounts. Accordingly, these patients may be further filtered to restrict inclusion, for example, to certain hospital or medical centers, clinical research consortiums, or geographic locations.
• Cohorts and cohorting features may be shared via the applications across different users or user groups to support multicentric studies or other analysis.
• User groups can be part of different medical institutions and cohorts, associated patients clinical and personal information can be shared via the application provided agreement between user groups.
• the complete data that can be shared for the patients includes but is not limited to:
• the present system may be configured to group patients in cohorts defined by a set of inclusion criteria as explained before. Accordingly, the system may include features to edit, delete, and/or duplicate cohorts. Provided that access is authorized for an existing cohort, the system may be configured to perform the following actions on the cohorts:
• Such actions may be initiated on a client device but may be executed and processed on a server.
• the application hosting server may be configured to edit, duplicate, and/or delete the cohort as a function of instructions received from the client device.
• the cohort may be defined by at least:
• scope for example, account versus network
• a cohort may be constituted by:
• the present system may be configured to generate and display comparisons of patient clinical data between different cohorts.
• Clinical data may include:
• Comparative visualization may include:
• the present system allows, given that a cohort has been created, to visualize and monitor the outcome of a cohort using the following metrics:
• the present system may be configured to stratify a cohort in two sub-groups based on the cohort outcome (PFS/OS), selected threshold (number of months or days), and may allow access to descriptive statistical insights of the cohort stratification.
• the present system allows, provided that a cohort of patients has been created, to split this cohorts into two sub cohorts based on a threshold value on the PFS or OS outcome and compare the outcome and descriptive statistical analysis of these two sub-cohorts.
• the present system may be configured to group patients in cohorts based on user defined or selected features.
• the system also provides the user with the ability to edit, delete, and/or duplicate cohorts.
• the cohort shall be defined by at least: an identifier and a name.
• Manual cohort features may be stored in the user group account (for example, institution or medical service) and may be accessible to all the users of the same group and may contain only patients belonging to the corresponding account.
• the user group account for example, institution or medical service
• a manual cohort can be shared with another account. However, such sharing may be permitted as a function of an agreement recorded from both institution and the patients of the cohort. In a further embodiment, such an agreement may manifest as a digital authentication key, wherein such a key permits extraction of data from patient records and/or permits inclusion of the patient’s data in cohort processing. Further, authentication keys and/or other digital permissions may exist between institutions and/or accounts. Thus, exchange of cohorts, modification of cohorts, and/or viewing of cohorts by other accounts may be permitted as a function of digital authentication permissions. For the purposes of this disclosure, such permissions may be those known to one of ordinary skill in the art.
• the present system may include access to the list of patients of an institution that are included in a given cohort. Accordingly, such a visualization may be generated on a client device (described in further detail below).
• the client device may be configured to display a graphical user interface, wherein the graphical user interface comprises one or more modules adapted to display the various metrics outputted by the system.
• the server may run predictions and the server processor may execute the actions described herein, wherein the server is further configured to deliver such resulting information to the client device.
• the various modules of the graphical user interface may be populated with the information originating from the server.
• the present system may be configured, when accessing a given cohort, to generate and display the list of patients included in the cohort.
• This information may comprise:
• the present system may be configured to stratify a cohort in two or more subgroups based on the cohort outcome (for example, Clinical Response at 1st evaluation), selected threshold (progression vs non-progression), and extract descriptive statistical insights of the cohort stratification.
• the present system may be configured to split this cohorts into two sub-cohorts based on a selection of categories of clinical response outcome and compare the outcome and descriptive statistical analysis of these two sub-cohorts.
• the present system may be configured to permit access to the latest patient enrollment status (number of total and new patients in user account and in the global cohorts).
• the present system may be configured to access patient cohorts (smart or manual) of which the selected patient is part of.
• patient cohorts smart or manual
• the matching may be performed in real-time, such that it is executed when the user is looking at a particular patient, given all the patient clinical data available at this date.
• Clinical study cohort update information
• the present system notifies the user on updates for user, user group, and clinical study cohorts, including differences in the number of patients in the cohort, identity, and characteristics of patients included or excluded from cohort since a previous update.
• Such updates may be delivered to a user’s client device upon occurrence of such an update.
• Such updates may cause the server, client device, and/or other component of the computing system to deliver a notification to the client device.
• the notification may be a text alert or another alert style known to those of ordinary skill in the art.
• the alert may inform the user of the nature of the update and/or the implications of such an update on patient monitoring or outcome predictions thereof.
• the present system may be configured to export and import smart cohort filters.
• the system allows sharing of the cohort filter definitions, between users, user groups, including across institutions. This feature may permit users to build on expert knowledge and enhances the process of filter creation.
• the present system when a given smart cohort has been created, may be configured to allow export of the inclusion criteria defined for this cohort (for example, in order to share it with another institution).
• the present system may be configured to create a cohort by importing the inclusion criteria that was shared to a user of another institution.
• the system may be configured to generate and display the list of cohorts that belongs to the user’s account , as well as other accounts that are accessible to the user or user group.
• the aforementioned list may provide information about each cohort, namely:
• Bookmarked status i.e., a user selected or curated sub-list of cohorts
• the present system may be configured to generate and/or predict comparisons of the outcome and descriptive analysis of these cohorts.
• Figure 4 displays another graph with a different set of criteria.
• the graph shows the PFS (Progression Free Survival) parameter expression level for the patients having received Immunotherapy only.
• the user selects the criteria to be displayed.
• the parameter displayed is PD-L1 expression level, Performance status at first evaluation, Performance status at diagnosis, PFS, overall survival Rate (OSR), Histopathology, Metastatic status, and/or Clinical response at first evaluation.
• the computer system of the invention may be configured to display in more detail the distribution of the clinical criteria among a plurality of patients.
• the window may be split in at least 2 windows (in a preferable embodiment, at least 3 windows). The number of windows displayed may be decided by the user.
• the selected patients of the cohort may be identic or distinct from the patients selected for other windows.
• the clinical parameter may be selected among the list comprising age at diagnostic, PD-L1 Expression level, Histopathology, ECOG at diagnosis, ECOG at first evaluation, Liver metastasis at baseline, Liver Metastasis at first evaluation, brain metastasis at baseline, brain metastasis at first evaluation, clinical response at first evaluation, progression free survival or Overall survival.
• the list of clinical parameters may also comprise radiomics features or genomics features (in particular, for example, the molecular profile such as KrasG mutational status).
• the computer system of the invention may be used for new hypothesis generation. By selecting various criteria among the list of clinical parameters or by selecting distinct group of patients selected by the treatment receive, the computer supports new hypothesis generation.
• the model may be a machine learning model or any other suitable predictive model.
• Figure 5 displays a window wherein the prediction of clinical outcome with the confidence number is shown.
• the window there is a 72% chance of progression at first evaluating whereby the patient is treated with Pembrolizumab monotherapy.
• a confidence interval of 95% is provided and may display the variation of the confidence number.
• the techniques described herein may also be implemented in electronic hardware, computer software, or any combination thereof. Such techniques may be implemented in any of a variety of devices such as general purposes computers, wireless communication device handsets, or integrated circuit devices having multiple uses including application in wireless communication device handsets and other devices. It can be executed as a stand-alone solution or in the cloud.
• the data storage as referenced in the above description can be a local storage, updated regularly from various sources of data, in particular other clinical personnel, or a cloud solution connecting the clinical personnel to a common platform. Any features described as modules or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices.
• the program code may be executed by a processor, which may include one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, an application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry.
• DSPs digital signal processors
• ASICs application specific integrated circuits
• FPGAs field programmable logic arrays
• a general -purpose processor may be a microprocessor; but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine.
• a processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
• processor may refer to any of the foregoing structure, any combination of the foregoing structure, or any other structure or apparatus suitable for implementation of the techniques described herein.
• functionality described herein may be provided within dedicated software modules or hardware modules.
• GUI graphical user interface
• the GUI generally refers to a system of interactive visual components for computer software that uses windows (or display), buttons, icons, menus, pointers and scroll bars (WIMPS) interface that are selectable or clickable by the user.
• Example of graphical user interface are including, without being limited to: Operating System such as Mac OS, Microsoft Word, GNOME, or Internet browsers, such as Internet Explorer, Chrome, and Firefox.
• a “display” refers to a computer output surface and projecting mechanism that shows text and/or graphic images to the computer user in a particular area on the screen.
• client devices 102-106 may include, for example, desktop computers, laptop computers, set top boxes, tablets, cell phones, smart phones, smart speakers, wearable devices (such as the Apple Watch) and the like.
• Servers 107-109 can include, for example, one or more application servers, content servers, search servers, and the like.
• Figure 13 also illustrates application hosting server 113.
• Figure 14 illustrates a block diagram of an electronic device 200 that can implement one or more aspects of an apparatus, system and method for validating and correcting user information (the “Engine”) according to one embodiment of the present disclosure.
• Instances of the electronic device 200 may include servers, e.g., servers 107-109, and client devices, e.g., client devices 102-106.
• a user may provide input via a touchscreen of an electronic device 200.
• a touchscreen may determine whether a user is providing input by, for example, determining whether the user is touching the touchscreen with a part of the user's body such as his or her fingers.
• the electronic device 200 can also include a communications bus 204 that connects the aforementioned elements of the electronic device 200.
• Network interfaces 214 can include a receiver and a transmitter (or transceiver), and one or more antennas for wireless communications.
• the memory 230 which can include Random Access Memory (RAM) 212 and Read Only Memory (ROM) 232, can be enabled by one or more of any type of memory device, e.g., a primary (directly accessible by the CPU) or secondary (indirectly accessible by the CPU) storage device (e.g., flash memory, magnetic disk, optical disk, and the like).
• the RAM can include an operating system 221, data storage 224, which may include one or more databases, and programs and/or applications 222, which can include, for example, software aspects of the program 223.
• the ROM 232 can also include Basic Input/Output System (BIOS) 220 of the electronic device.
• BIOS Basic Input/Output System
• Software aspects of the program 223 are intended to broadly include or represent all programming, applications, algorithms, models, software and other tools necessary to implement or facilitate methods and systems according to embodiments of the present disclosure.
• the elements may exist on a single computer or be distributed among multiple computers, servers, devices or entities.
• the power supply 206 contains one or more power components, and facilitates supply and management of power to the electronic device 200.
• the input/output components can include, for example, any interfaces for facilitating communication between any components of the electronic device 200, components of external devices (e.g., components of other devices of the network or system 100), and end users.
• components can include a network card that may be an integration of a receiver, a transmitter, a transceiver, and one or more input/output interfaces.
• a network card for example, can facilitate wired or wireless communication with other devices of a network. In cases of wireless communication, an antenna can facilitate such communication.
• some of the input/output interfaces 240 and the bus 204 can facilitate communication between components of the electronic device 200, and in an example can ease processing performed by the processor 202.
• the electronic device 200 can include a computing device that can be capable of sending or receiving signals, e.g., via a wired or wireless network, or may be capable of processing or storing signals, e.g., in memory as physical memory states.
• the server may be an application server that includes a configuration to provide one or more applications, e.g., aspects of the Engine, via a network to another device.
• an application server may, for example, host a web site that can provide a user interface for administration of example aspects of the Engine.
• Servers may vary widely in configuration and capabilities, but they generally include one or more central processing units, memory, mass data storage, a power supply, wired or wireless network interfaces, input/output interfaces, and an operating system such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, and the like.
• client devices may include, for example, any computing device capable of sending and receiving data over a wired and/or a wireless network.
• client devices may include desktop computers as well as portable devices such as cellular telephones, smart phones, display pagers, Radio Frequency (RF) devices, Infrared (IR) devices, Personal Digital Assistants (PDAs), handheld computers, GPS-enabled devices tablet computers, sensor-equipped devices, laptop computers, set top boxes, wearable computers such as the Apple Watch and Fitbit, integrated devices combining one or more of the preceding devices, and the like.
• RF Radio Frequency
• IR Infrared
• PDAs Personal Digital Assistants
• handheld computers GPS-enabled devices tablet computers
• sensor-equipped devices sensor-equipped devices
• laptop computers set top boxes
• wearable computers such as the Apple Watch and Fitbit, integrated devices combining one or more of the preceding devices, and the like.
• Client devices such as client devices 102-106, as may be used in an example apparatus, system and method embodying the Engine, may range widely in terms of capabilities and features.
• a cell phone, smart phone or tablet may have a numeric keypad and a few lines of monochrome Liquid-Crystal Display (LCD) display on which only text may be displayed.
• LCD monochrome Liquid-Crystal Display
• a Web-enabled client device may have a physical or virtual keyboard, data storage (such as flash memory or SD cards), accelerometers, gyroscopes, respiration sensors, body movement sensors, proximity sensors, motion sensors, ambient light sensors, moisture sensors, temperature sensors, compass, barometer, fingerprint sensor, face identification sensor using the camera, pulse sensors, heart rate variability (HRV) sensors, beats per minute (BPM) heart rate sensors, microphones (sound sensors), speakers, GPS or other location-aware capability, and a 2D or 3D touch- sensitive color screen on which both text and graphics may be displayed.
• data storage such as flash memory or SD cards
• accelerometers such as flash memory or SD cards
• gyroscopes such as accelerometers, gyroscopes, respiration sensors, body movement sensors, proximity sensors, motion sensors, ambient light sensors, moisture sensors, temperature sensors, compass, barometer, fingerprint sensor, face identification sensor using the camera, pulse sensors, heart rate variability (HRV) sensors, beats per minute (BPM) heart
• Client devices such as client devices 102-106, for example, as may be used in an example apparatus, system and method implementing the Engine, may run a variety of operating systems, including personal computer operating systems such as Windows, iOS or Linux, and mobile operating systems such as iOS, Android, Windows Mobile, and the like. Client devices may be used to run one or more applications that are configured to send or receive data from another computing device. Client applications may provide and receive textual content, multimedia information, and the like. Client applications may perform actions such as browsing webpages, using a web search engine, interacting with various apps stored on a smart phone, sending and receiving messages via email, SMS, or MMS, playing games (such as fantasy sports leagues), receiving advertising, watching locally stored or streamed video, or participating in social networks.
• games such as fantasy sports leagues
• one or more networks may couple servers and client devices with other computing devices, including through wireless network to client devices.
• a network may be enabled to employ any form of computer readable media for communicating information from one electronic device to another.
• the computer readable media may be non-transitory.
• a network may include the Internet in addition to Local Area Networks (LANs), Wide Area Networks (WANs), direct connections, such as through a Universal Serial Bus (USB) port, other forms of computer-readable media (computer- readable memories), or any combination thereof.
• LANs Local Area Networks
• WANs Wide Area Networks
• USB Universal Serial Bus
• a router acts as a link between LANs, enabling data to be sent from one to another.
• Communication links within LANs may include twisted wire pair or coaxial cable, while communication links between networks may utilize analog telephone lines, cable lines, optical lines, full or fractional dedicated digital lines including Tl, T2, T3, and T4, Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines (DSLs), wireless links including satellite links, optic fiber links, or other communications links known to those skilled in the art.
• ISDNs Integrated Services Digital Networks
• DSLs Digital Subscriber Lines
• wireless links including satellite links, optic fiber links, or other communications links known to those skilled in the art.
• remote computers and other related electronic devices could be remotely connected to either LANs or WANs via a modem and a telephone link.
• a wireless network may further include an autonomous system of terminals, gateways, routers, or the like connected by wireless radio links, or the like. These connectors may be configured to move freely and randomly and organize themselves arbitrarily, such that the topology of wireless network may change rapidly.
• a wireless network may further employ a plurality of access technologies including 2nd (2G), 3rd (3G), 4th (4G) generation, Long Term Evolution (LTE) radio access for cellular systems, WLAN, Wireless Router (WR) mesh, and the like.
• Access technologies such as 2G, 2.5G, 3G, 4G, and future access networks may enable wide area coverage for client devices, such as client devices with various degrees of mobility.
• a wireless network may enable a radio connection through a radio network access technology such as Global System for Mobile communication (GSM), Universal Mobile Telecommunications System (UMTS), General Packet Radio Services (GPRS), Enhanced Data GSM Environment (EDGE), 3 GPP Long Term Evolution (LTE), LTE Advanced, Wideband Code Division Multiple Access (WCDMA), Bluetooth, 802.1 Ib/g/n, and the like.
• GSM Global System for Mobile communication
• UMTS Universal Mobile Telecommunications System
• GPRS General Packet Radio Services
• EDGE Enhanced Data GSM Environment
• LTE Long Term Evolution
• LTE Advanced Long Term Evolution
• WCDMA Wideband Code Division Multiple Access
• Bluetooth 802.1 Ib/g/n, and the like.
• a wireless network may include virtually any wireless communication mechanism by which information may travel between client devices and another computing device, network, and the like.
• IP Internet Protocol
• the Internet includes local area networks (LANs), Wide Area Networks (WANs), wireless networks, and long-haul public networks that may allow packets to be communicated between the local area networks.
• the packets may be transmitted between nodes in the network to sites each of which has a unique local network address.
• a data communication packet may be sent through the Internet from a user site via an access node connected to the Internet.
• the packet may be forwarded through the network nodes to any target site connected to the network provided that the site address of the target site is included in a header of the packet.
• Each packet communicated over the Internet may be routed via a path determined by gateways and servers that switch the packet according to the target address and the availability of a network path to connect to the target site.
• the header of the packet may include, for example, the source port (16 bits), destination port (16 bits), sequence number (32 bits), acknowledgement number (32 bits), data offset (4 bits), reserved (6 bits), checksum (16 bits), urgent pointer (16 bits), options (variable number of bits in multiple of 8 bits in length), padding (may be composed of all zeros and includes a number of bits such that the header ends on a 32 bit boundary).
• the number of bits for each of the above may also be higher or lower.
• Such services may make use of ancillary technologies including, but not limited to, “cloud computing,” distributed storage, DNS request handling, provisioning, data monitoring and reporting, content targeting, personalization, and business intelligence.
• a CDN may also enable an entity to operate and/or manage a third party's web site infrastructure, in whole or in part, on the third party's behalf.
• a Peer-to-Peer (or P2P) computer network relies primarily on the computing power and bandwidth of the participants in the network rather than concentrating it in a given set of dedicated servers.
• P2P networks are typically used for connecting nodes via largely ad hoc connections.
• a pure peer-to-peer network does not have a notion of clients or servers, but only equal peer nodes that simultaneously function as both “clients” and “servers” to the other nodes on the network.
• Embodiments of the present disclosure include apparatuses, systems, and methods implementing the Engine. Embodiments of the present disclosure may be implemented on one or more of client devices 102-106, which are communicatively coupled to servers including servers 107-109. Moreover, client devices 102-106 may be communicatively (wirelessly or wired) coupled to one another. In particular, software aspects of the Engine may be implemented in the program 223. The program 223 may be implemented on one or more client devices 102-106, one or more servers 107-109, and 113, or a combination of one or more client devices 102-106, and one or more servers 107-109 and 113. [0198] In an embodiment, the system may receive, process, generate and/or store the clinical data.
• the system may include an application programming interface (API).
• the API may include an API subsystem.
• the API subsystem may allow a data source to access data.
• the API subsystem may allow a third-party data source to send the data.
• the third-party data source may send JavaScript Object Notation (“JSON”)-encoded object data.
• JSON JavaScript Object Notation
• the object data may be encoded as XML-encoded object data, query parameter encoded object data, or byte-encoded object data.
• the invention of the present disclosure may be a computer system for monitoring clinical outcome, comprising a display screen and connecting at least two databases, at least one database comprising clinical data of patients, including diagnostic status, and a second database comprising a set of display parameters for each diagnostic status.
• the first database and the second database may be stored on one or more servers 107-109.
• a user may utilize a client device 102-106 and may be in informatic communication with the first and second database via the wireless network 110 and/or the LAN 112.
• the first and second database may be stored in separate servers 107-109 (or other computing devices).
• the first database comprising clinical data may be stored in a clinic server
• the second database comprising display characteristics for each diagnostic status may be stored in a server operated by the system administrator of the instant computer system.
• the computer system for example, via the processor of a user device, may be configured to execute the steps of obtaining the clinical data for a selected patient.
• the client device 102-106 may transmit a request for clinical data based on associations with a selected patient.
• the request may be evaluated by one or more servers 107-109.
• the computer system may be configured to obtain a set of display parameters based on the diagnostic status of the patient.
• the client device 102-106 may be adapted to generate a signal and/or otherwise request the set of display parameters from the corresponding database and/or server 107-109.
• the databases may include separate permissions and/or authentication processes. Accordingly, the computer system may be configured to aggregate such data via electronic communication with the servers 107-109. However, in an embodiment utilizing an application hosting server 113, the client device 102-106 may communicate with the application hosting server 113, and the application hosting server 113 may more directly communicate with the servers 107-109. Thus, the application hosting server 113 may act as a bridge between the servers 107-109 and the client devices 102-106. [0205] The processor of the client device 102-106 may be configured to display on a first window the data of the patient according to the set of display parameters. The client device 102-106 may include an image display (such as an LCD screen) configured to generate and display a GUI.
• an image display such as an LCD screen
• the GUI may comprise at least a clinical identifier of the selected patient; a diagnostic status of the selected patient; and/or a timeline of selectable events associated with the selected patient.
• each selectable event may be further displayed in additional windows with the detailed metrics of the patient for the selected event.
• the computer system may include a client device in communication with one or more databases where the one or more databases are stored in one or more servers.
• the computer system further comprises connection to a third database.
• the third database may comprise at least one set of cohorting parameters, wherein the computer system may execute the additional step of selecting at least one clinical parameter of the selected patient to be compared with the corresponding clinical parameter of a cohort.
• the clinical parameter may be selected manually by a user.
• the clinical parameter may be determined and selected by a software element of the computer system.
• the cohort may be formed according to a clinical parameter generated by a machine learning element or another predictive model aspect.
• the computer system includes and/or is in communication with a client device 102-106, the client device 102-106 may transmit a request to one or more of the servers 109.
• the request may induce retrieval of clinical data of the plurality of patients comprised in the cohort.
• the system for example via the client device or a server, may represent the distribution of the selected clinical parameter of the cohort.
• the client device processor or server processor may be configured to generate a representation of the distribution of the selected clinical parameter of the cohort.
• either the server processor and/or client device processor may be configured to position the selected clinical parameter of the selected patient in the distribution.
• the client device may display in a window the distribution of the clinical parameters of the cohort.
• a server may be adapted to input the data for the selected patient into the trained model for prediction of clinical outcome according to the diagnostic status of the patient. Consequently, the server may generate a prediction of clinical outcome. For example, by running the trained model and generating the prediction on the server, the client device may experience reduced processing loads. Thus, while the client device is configured to receive input from a user and output the generated results, the client device may not be bogged down by the intensive processing that is occurring externally. The client device may display, via the GUI, a fourth window showing the prediction of clinical outcome and confidence of the model.
• each server 107-109 may correspond to one or more of EHR records, biological data, genomic data, radiological data, clinical data, predicted data, and/or other categories of data. Further, one or more of the servers 107-109 may be operated by other users, clinics, etc. Thus, sensitive information may be exchanged between the servers 107-109 and the application hosting server 113. Such sensitive information may be utilized by the application hosting server 113 to generate predictions and/or pictorial representations of various metrics, but may not expose said sensitive information (for example, identifying portions thereof) to the client device 102-106. Therefore, the distributed computer network described above may both (1) spread intensive processing loads across various servers and computing devices; and (2) allow secure exchange of information via a dedicated application hosting server.
• the invention of the present disclosure may be a system of networked devices configured to monitor patient progress and evaluate outcomes thereof.
• a system may comprise a client device comprising at least one device processor, at least one display, at least one device memory comprising computer-executable device instructions which, when executed by the at least one device processor, cause the client device to receive requests from a user and/or display results from the server.
• the system may further include a server in bidirectional communication with the client device, the server may comprise at least one server processor, at least one server database, at least one server memory comprising computer-executable server instructions which, when executed by the at least one server processor, cause the server to recall data specific to a particular patient, gather information pertaining to a cohort, predict outcomes of a patient and/or a cohort, and deliver such information and/or results to the client device.
• the server may comprise at least one server processor, at least one server database, at least one server memory comprising computer-executable server instructions which, when executed by the at least one server processor, cause the server to recall data specific to a particular patient, gather information pertaining to a cohort, predict outcomes of a patient and/or a cohort, and deliver such information and/or results to the client device.
• the various actions described herein may be executed by any suitable computing device.
• the actions, software aspects, and/or other methods described herein may solely be executed and processed via a singular device.
• the methods and features of the computing network described herein may be executed by one of a client device, an application hosting server, and a server.
• inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single inventive concept if more than one is in fact disclosed.
• inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single inventive concept if more than one is in fact disclosed.
• inventive subject matter merely for convenience and without intending to voluntarily limit the scope of this application to any single inventive concept if more than one is in fact disclosed.

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