Patent ID: 12224070

DETAILED DESCRIPTION

Reference is first made toFIG.1, which illustrates schematically the overall structure of an exemplary implementation of the disclosed invention. A method detects individuals having characteristics that indicate a specific disease process. In a first phase of the method, historical patient data from electronic medical records (EMR), electronic health records (EHR), claims data or data from other sources are collected, followed by application of machine/deep learning, natural language processing (NLP), or other individual or combined machine learning techniques to train an algorithm of the method to identify subjects with the autoimmune conditions which are to be diagnosed based on known cases of such disease in the historic population data. In a second phase, new patient data are input to the algorithm to enable determination of the probability and risk that a given individual in the new population has an autoimmune condition. Specifics of this process are delineated for an exemplary implementation of the method: in the example provided here, the method determines the probability of a given individual having celiac disease or another gastrointestinal autoimmune disorder, either currently or predicted to develop within a future time frame.

In block101, a historic database of insurer medical claims and/or EMR data for a large population, representing the target population for this algorithm, is accessed to provide examples for training the models of the system. This data is augmented with additional sources, such as IOT sensor data, subject provided information, and aggregated statistics relevant to target subjects collected either from research datasets, or via use of the proposed system. This information is used in subsequent steps103and106ato generate processed and filtered training information, ultimately for use in step109.

In step103, the large population data from block101is used in combination with rules derived from medical experts or known medical protocols, here referred to as “expert medical logic”102, to generate tagged or labeled training data of subjects. Expert medical logic, entered into the system, is a database of rules providing specific logic how to classify subjects retrospectively, based on the data provided. This logic is based on interviews with medical doctors and information collected from research papers that enable the system to classify retrospectively who has been diagnosed with which diseases, so that this classification can be used to train the artificial intelligence classifier. Data tagging, in the context of this application, is the process of classifying and tagging data samples to label the historic population data with the target autoimmune diagnoses. The system uses the expert medical logic to retroactively identify and label each person's medical history with the autoimmune conditions that he has been later positively diagnosed with. The data is separated and a tag assigned to it prior to the diagnosis. The tagged training data will be used in subsequent steps to learn how to classify and predict the risk of having such conditions via analyzing patient data prior to the diagnosis.

In step104, the large dataset of patient files is utilized to train a “feature embedding model”. The feature embedding model is a machine learning transformation that converts the patient data into a finite vector of real numbers. The vector space is of lower dimension than the entire patient data and therefore compresses the data keeping the important aspects and features that enable subject classification and diagnosis but also makes similar patients convert into vectors with a small distance between them. This transformation generates a representation of the data that is easier to classify and can better classify new subjects it has not seen before. This method is known as self-supervised representation learning and is used to generate an embedding model and optimize its parameters. Supervised learning and self-supervised representation learning are two different deep learning mechanisms. Supervised learning uses many classified examples to train the algorithm to correctly classify new samples based on multi-variate similarity to the training samples. Self-supervised learning is unsupervised learning in which the algorithm is trained to identify key differentiating features between classes of subjects, by going over many unclassified patient medical data files and studying the relationship between different segments or views of the medical files presented to it.

In this application, the embedding layer is a low-dimensional space for creating a dense encoding that represents the subject's medical history. This model is trained and generated using self-supervised learning and optimized over a large training set of historical medical data collected from a large population in step101. The embedding for autoimmune disease diagnosis captures the semantics of the input from step101, e.g., a variety of background data, comprising both medical data, environmental conditions, and patient risk factors, by placing semantically similar inputs close together in the embedding space. Although the embedding model itself may be reused among various populations, the subject population to which the method will be applied in steps106bto109should be similar to, or derived from, the larger general population in block101, such that the embedding parameters accurately distinguish among healthy individuals and those with a specific autoimmune diagnosis in that population. This is important because normal ranges of lab values and ways in which autoimmune conditions appear may differ among various populations. The embedding model parameters generated in step104are then input to step106ato embed the tagged training data. The relevant patient data features selected for training are defined by current legacy methods, based on at least two of published medical literature, diseases registries, medical practice guidelines and the medical data.

In step106a, the tagged history data of all of the recorded subjects, is passed through the feature embedding mechanism, loaded with the model derived in step104, and is then converted into tagged feature vectors for training107a.

In step108a, a multi-output classifier model is trained using supervised learning of the tagged training data (107a). The steps101to108a, shown inFIG.1within the dotted line100, are steps used for the periodic training of the artificial intelligence models using the large historic population data. Steps106bto108b, on the other hand, are steps in which the feature embedding and classifying of the subject data are applied to the data of the currently analyzed patients, whose diagnoses are being resolved.

The output from step108acomprises multi-label classifier model parameters, which are also used to classify current patient data vector107bin step108b. Multi-label classification is a classification mechanism that outputs multiple results associated with the likelihood of the inspected object being of a specified class. The classifier classifies object into multiple classes based on the input features of the object. In the context of this disclosure, the classifier provides probabilities of the analyzed person having: any autoimmune disease, any gastrointestinal autoimmune disease, or a specific autoimmune disorder, based on features found in his collection of medical records and data.

The embedding model parameters output from the self-supervised learning in step104are also used as input model for step106b. Additional input for step106bcomprises raw data on a current subject's present situation and recent history from a variety of sources. The raw data may comprise at least some of patient insurance claims, electronic medical record data, and information gleaned or acquired from IoT, sensors, and health app data105. In this step, the system applies the embedding parameters developed in step104to the raw data from block105and the output is a personal feature vector107brepresenting the data of the current subject. This output is then used as the input for the multi-label classifier model of step108b.

In step108b, the model parameters developed in108aare used to classify the personal feature vector (107b).

Step109uses the output from step108bto generate a corresponding diagnosis probability vector with multiple values associated with a patient's file, that provides a probability that the current subject has each condition analyzed, such that further diagnosis recommendations and treatment recommendations can be derived. Each value in the vector corresponds to one of the autoimmune conditions that the system is programmed to seek, with individual values representing the likelihood of the person having the associated autoimmune disease or condition. Usually, the system will compare these values to a threshold for exceeding or going below the pre-defined normal range, and when the threshold has been crossed, suggesting the possibility of a disease state, the system will generate an indication or alert. This process is explained in more detail inFIG.6.

In the event that no diagnosis is made, step109may also provide output indicating the likelihood that the given individual may develop an autoimmune disease in the future.

Finally, in step110, the doctor or other health care provider, generates retrospective feedback on the diagnostic accuracy of the output generated by the system. The physician's analysis of the system's performance is input to the expert medical logic database of step102, to update and improve that data.

In other implementations of the disclosed methods, the algorithm is able to provide from steps109and110, treatment recommendations, referral suggestions, or follow-up advice, as will be further delineated inFIG.4.

The following general CD parameters used for diagnosis, where CD is used as an exemplary disease for implementing this method, refer to the process described inFIG.1. Example of parameters or features from the patient's data file, used in the machine learning algorithm may fall into the following categories: demographics including family history of CD or other gastrointestinal conditions, symptoms, concurrent diagnoses, lab tests, medications, procedure and current and past measurements such as height, weight, and BMI. A large number of parameters may be used in training the algorithm; over time, additional, different, or fewer parameters may be incorporated to improve the diagnostic accuracy of the method. Each of these categories are further defined and detailed below. Additional categories and additional parameters within each category may be included over time as the machine learning algorithm identifies and correlates other factors as having relevancy to the diagnosis of CD. Demographics includes gender, birth season, and age at the time of the test and, if known, age at the time of CD diagnosis.

Symptoms included are collected from the patient's historical data up to a predefined time window, before medical diagnosis of this condition actually took place for that patient. Specific relevant symptoms comprise those relating to abdominal pain; bloating (abdominal swelling); constipation; diarrhea; fatigue; headaches or migraines; weight loss; bone or joint pain; depression or anxiety; irritability and behavioral issues; peripheral neuropathy (tingling, numbness or pain in the hands and feet); seizures; skin rash; canker/ulcer sores inside the mouth; vomiting; pale, foul-smelling, or fatty stools; and acid reflux. Further symptoms may be included over time as the algorithm improves its specificity and accuracy, and is able to incorporate additional symptom patterns and correlate them with the diagnosis of CD.

Laboratory tests and measurements such as height, weight, and BMI include the minimum values, maximum values, and the first and last in the predefined time, e.g., 5 years, preceding the examination. For children, growth measurements over time are an important input to the system. The selected laboratory blood tests with relevance for diagnosis of CD are shown below, ALT (alanine aminotransferase, an indicator of liver damage); AST (aspartate aminotransferase, an indicator of liver damage); GGT (gamma-glutamyl transpeptidase); CRP (C-reactive protein); ESR (erythrocyte sedimentation rate); ferritin (a protein that stores iron in cells); folic acid; Hb (hemoglobin); MCV (mean corpuscular volume); RDW (red cell distribution width); HLA DQ2 and/or HLA DQ8. Also included in the category of laboratory tests are identification of anemia; size and volume of red blood cells; measuring enzymes responsible for liver function; and levels of vitamins in the blood, at least vitamin A; vitamin B12, and vitamin D. Further laboratory tests of the blood or other body fluids may be included over time as the algorithm improves its specificity and accuracy, and is able to incorporate additional lab values and correlate them with the diagnosis of CD.

Concurrent diagnoses associated with CD that may predispose to a diagnosis and are thus included in the algorithm include the following: acute gastroenteritis; attention deficit hyperactivity disorder (ADHD); alopecia (hair loss/baldness); anemia; aphthous stomatitis; autoimmune hepatitis; autoimmune thyroiditis (Hashimoto's disease); arthritis; infection withCampylobacter jejuni(a bacterium that causes inflammation of the bowel and diarrhea); dental enamel defects; dermatitis; herpetiformis enteritis due to rotavirus (a virus that causes severe diarrhea in children and infants); failure to thrive; giardiasis (a common parasitic disease manifested in diarrhea, abdominal pain, weight loss, vomiting, etc.); Helicobacter pylori infection of the gastric mucosa; herpetiformis dermatitis, a chronic skin disease manifested in blisters; IBD; infertility; recurrent miscarriage; missed menstrual periods; lactose intolerance; liver and biliary tract disorders (elevated transaminases, fatty liver, primary sclerosing cholangitis, etc.); osteoporosis or osteopenia; short statue; Type 1 diabetes; vitiligo; peripheral neuropathy (tingling, numbness or pain in hands or feet); skin and hair findings such as thin or damaged hair, brittle nails, or onychomycosis; autoimmune thyroiditis; and chronic hypertransaminasemia (elevated liver enzymes).

CD has a genetic component in that individuals with specific HLA alleles, i.e., DQ2 and DQ8, have an increased risk of developing CD (3% vs. 1% in the general population). Thus, the HLA haplotype for each individual may also be included as a parameter. Further diagnoses may be included over time as the algorithm improves its specificity and accuracy, and is able to incorporate additional results and correlate them with the diagnosis of CD.

Medications specifically included in the algorithm may include antibiotics (IV and PO); H2 receptor antagonists, which block histamines and remove acidity in the stomach; NSAIDs (nonsteroidal anti-inflammatory drugs); paracetamol; PPI (proton pump inhibitors, which inhibit acid secretion in the stomach); and steroids (IV and PO), which may damage the GI tract lining. Further medications and other routes of administration may be included over time as the algorithm improves its specificity and accuracy, and is able to incorporate additional findings and correlate them with the diagnosis of CD.

Objective measurements or values derived from measurements included in the algorithm include height (decrease in percentile, based on the z-score); weight; weight loss; BMI (either the numerical value, or a Boolean cutoff for normal); [current BMI]/[BMI when CD was diagnosed]; [current weight]/[weight when CD was diagnosed]; and [current height]/[height when celiac was diagnosed]. As with other parameter categories described above, further measurements may be included over time as the algorithm improves its specificity and accuracy, and is able to incorporate additional findings and correlate them with the diagnosis of CD.

In the initial iterations of the algorithm as it is being trained, inclusion criterion for subjects as having a diagnosis of CD can be based on the current standard of care for diagnosis of CD. The following section explains the procedures used in the use of expert medical logic and the tagging of CD patients based on the historic data, as implemented in step304. The first level of diagnosis is a blood test called the tTG-IgA test, which detects antibodies to tissue transglutaminase. This test will be positive in about 98% of patients with celiac disease who are on a gluten-containing diet; results will be negative in about 95% of healthy people without CD, meaning that the results are not 100% accurate for either diagnosing or for ruling out a diagnosis of CD. The gold standard diagnostic tool is an endoscopic biopsy of the small intestine, which in positive cases shows inflammation and damage to the ciliated lining of the small intestine, leading to poor nutrient absorption. Results of the biopsy will be available in a subset of individuals having a positive tTG-IgA test, further confirming the diagnosis in those subjects. However, anyone with a positive tTG-IgA test will be considered to have CD, for the purposes of algorithm training. In cases where EMR data and lab test results are not available, use is made of medical diagnostic procedure codes from insurance or other claims indicating that the tTG-IgA test, or upper gastrointestinal endoscopy, has been performed; suspected medical diagnostic codes for celiac disease in more than one medical insurance claim are taken as an indication of a positive diagnosis of celiac disease.

Based on these initial results, subjects are divided into two groups. The treatment group is comprised of those individuals having a positive tTG-IgA test, or, in the event that there are no positive test results, similar indications mentioned above for insurance claims data; the control group is comprised of those having a negative tTG-IgA test result. Subjects who reach the diagnostic criteria for having CD are used to establish the ‘ground truth’, i.e., results of patients who have been historically diagnosed with CD. Ground truth refers to a dataset with accurate tagging that is used to train the model and test it, as the expected result is known to be accurate. In implementations of the present disclosure, ground truth is generated from the historic patient data files by identifying those files that have clear indication of positive diagnosis of specific diseases or clear indication of no disease. The system separates those files into data collected at a predefined time prior to the time of diagnosis and into target diagnosis tagging that embodies the correct diagnosis as later found for that subject.

In cases where specific diagnostic test results are not available, e.g. insurance claims without lab results, the ‘ground truth’ can be defined by identifying cases where a specific diagnosis of, for instance, suspected celiac disease, appears in the claims data at a later time after procedures or tests related to such a diagnosis have been performed. For example, claims for blood tests for tTG-IgA or gastro-endoscopy, followed later by claims including celiac diagnosis, would indicate that tests have had a positive outcome.

FIG.2provides further details of the machine learning and other artificial intelligence procedures incorporated in the feature embedding model developed in steps101to104ofFIG.1.

In step201, data are input from a large historic database of different medical, health and claims data collected per subject of a large population. These data are pre-processed to standardize, normalize and remove/fill missing values, a process that enhances the quality and quantity of information available to use for training, and upon which to base subsequent decisions.

In step202, the input data is processed to generate training data for a self-supervised task. These tasks may include prediction of parts of the patient record based on another known part of that record, identifying randomly added, changed or removed data points in the medical record, or similar tasks that enable the model to learn a compact representation of the input data file via a smaller vector of real numbers. These patient data vectors are optimized in such a way that information located in proximity in the embedding space represents a similar level of risk with respect to the diagnostic probability of a given subject for developing the autoimmune disease under consideration.

In step203, this embedding model is trained on a very large data set with self-supervised target outputs, its output providing the parameters for the embedding model. In other words, the embedding model transformation parameters are optimized so that the embedding vectors created will represent in a compact way the data features needed for diagnosis.

In step204, the method determines if the required level of accuracy has been reached by measuring the accuracy achieved in the self-supervised training tasks. If not, the method returns to step203and refines the parameters with additional optimization cycles. If the required level of accuracy has been reached, the method proceeds to step205, wherein the system exports the embedding model parameters to the classifier embedding modules inFIG.1, steps106aand106b.

Reference is now made toFIG.3, which explains the data handling procedures shown in the previous drawings in further detail, using an exemplary implementation of the method for predicting and diagnosing CD. The algorithm details sub-steps specifically for determining the probability of a given individual to have a positive transglutaminase antibody result indicating CD autoimmunity. It is to be understood that the same process may be applied to other medical data with predictive value for a given autoimmune disease, such as lab values, genetic biomarkers, or imaging studies.

Steps301to303delineate individual steps used in treatment of historical data fromFIG.1step101andFIG.2step201. Step305relates to the periodic training of the artificial intelligence model100inFIG.1; similarly, the output of step307corresponds to the application of the multi-label classifier parameters derived inFIG.1step108ato the individual subject data in step108b.

In step301, string-type data is standardized to categorical data. A string is a data type used in programming that is used to represent text rather than numbers, comprised of a set of characters. In this application, the word “autoimmune” and the phrase “gastrointestinal autoimmune disorder” are both strings. By contrast, categorical data have a limited, and usually fixed, number of possible values, e.g., assigning each individual to a particular group, such as “normal”, “celiac disease”, or “at risk for celiac disease”, on the basis of the diagnosis probability vector. Data is collected from a source such as EMR, or from other sources such as a survey that is completed by the individuals or by an application such as the Apple Health App, which electronically collects health-related data from other applications and sources.

In step302, each data point is annotated as present, missing or censored. Missing information is then used as data during the model learning by noting its absence in a separate feature and taking a median value for that data point from among all data sets in the relevant population, which comprises the data source. Features which comprise the algorithm inputs are determined, and cutoff values are selected for being outside the normal range and indicating a possible diagnosis of CD.

In step303, all missing or censored data points are allocated the median of non-missing data points to complete the data set without changing its distribution. Features with continuous values (e.g. lab test numeric values) are normalized based on their common distribution in the population.

In step304, which corresponds to step103ofFIG.1, the system uses expert medical logic to retroactively tag the historic data of each subject according to all autoimmune diseases that have been later diagnosed for this patient (based on more recent data collected). Using the diagnosis tagging, the system creates training vectors based on the historic data (prior to diagnosis), which, when added together with the correct diagnosis tagging, represents the desired classifier output.

In step305, new subject data is entered and undergoes feature embedding. The embedding transformation converts the long vector of input features into a smaller embedding vector using the embedding model parameters from step205. The results are training vectors, in which patients with similar conditions related to autoimmune diseases have similar vectors, making the training phase more efficient. An exemplary graph illustrating training vectors and new patient vectors is further delineated inFIG.6.

In step306, which corresponds to the periodic training steps,100, of the artificial intelligence model inFIG.1, the algorithm is trained and tested iteratively using supervised learning of the tagged training vectors and testing on the control group or validation set, as described in the periodic training steps of the artificial intelligence model100ofFIG.1, until the algorithm performs satisfactorily; the results should match the ground truth results according to the sensitivity and specificity pre-defined for the diagnosis classifier.

In step307, the method determines if the required level of diagnostic accuracy has been reached; if not the method continues the supervised learning process of306. If the required level of diagnostic accuracy has been reached, the method proceeds to step308.

In step308, the model is tested and validated using validation training samples set aside for the validation phase. The best model hyper-parameters, chosen to optimize the system performance using designated training vectors, are selected based on the validation set results, and the final performance evaluation is performed on a preselected test set. Hyperparameters, in machine learning, are structural parameters of the algorithm whose values are set before the learning process begins. By contrast, the values of other AI model parameters, sometimes called weights or factors in neural network architectures, are derived via training. Both of these types of ‘parameters’ are in distinction to the medical parameters or clinical features, referred to elsewhere in the present disclosure, that are used to define a subject's susceptibility or probability of developing a specific autoimmune disease.

Reference is now made toFIG.4, a schematic representation of an implementation of the method for interventional recommendations. The steps within the dotted line400represent periodic training of artificial intelligence models. In block403, an intervention recommendation model is developed, using supervised learning by examples. The training inputs for this model are examples generated from the population medical record database401using medical guidelines402, and by collecting patients' response to specific treatments and scoring them accordingly. The information in steps401and402may be the same or different as that inFIG.1steps101and102. These scores are used as target results to train the algorithm. After the model400is developed through machine learning or other form of artificial intelligence, the recommendation model parameters are input into the intervention recommendation model406. Other inputs to the model406are the patient diagnosis probability vector from step110inFIG.1, and patient historical data405, comprised of previous tests and procedures, which may be the same data as provided inFIG.1, step105. The output of the intervention recommendation model is a ranked list of follow-up and/or treatment recommendations in step407. Additionally, to the routine output in step407, in step408, the doctor or other health care provider can input retrospective feedback on the diagnostic accuracy of the output generated by the system. This information is used to improve the expert medical logic in step402.

Reference is now made toFIG.5, showing a description of how the algorithm operates within the full diagnostic system. Once the algorithm is fully trained and validated as described inFIG.3, it may be used on other populations of undiagnosed individuals for screening and detection purposes. In this method, for the example of CD assessment, the algorithm calculates the probability of each given individual to have a positive TTG-IgA result, and notifies the software operator of cases reaching a specific threshold of probability, as described below. Image processing of small intestinal biopsy tissue slides from individuals with a high predictive probability of having or developing CD may be used to compare with images from individuals having previously been diagnosed with CD using small intestinal biopsy.

In step501, individual data are aggregated into a personal patient data source. In step502, the algorithm analyzes or processes each patient data set. In step503, the algorithm calculates the probability of each subject having CD or other chronic, gastrointestinal autoimmune disease, by integrating the vectors for beyond-threshold values of any number of tests that fall outside the normal range. At this step, if active learning is used, the system may indicate need for additional medical information or request additional data from the subject. Active learning is a machine learning training method where the algorithm provides questions or suggests collection of additional data in order to improve its ability to provide specific and accurate diagnosis. The method analyzes the input patient vector to be classified, and if the vector falls in a “gray area” where the diagnosis is not clear, it will request additional information or data, such as for instance, a lab test result or a question to the subject about missing data. Following input of answers to these requests, the algorithm will be in a better position to provide a clear and more probable diagnosis.

In step504, the system provides an alert when the probability of a given subject having one of the defined gastrointestinal autoimmune diseases, exceeds a predefined threshold. If the user requests more details, the system can provide explainability analysis of its decision, by means of identifying important parameters leading to its diagnosis decision. Explainability refers to mechanisms of analyzing the operation of machine learning, or other types of AI-based decision support algorithms, and presenting to the user how the recommendation has been reached and what parameters have most influenced this decision. The goal of these mechanisms is to build trust in the system's correctness by enabling an expert user to trace the decision factors and logic of the results and also enables effective human oversight of the process.

In step505, the method determines whether a new diagnosis has been made. If not, the method proceeds to analyze the data of the next subject by returning to step501. If a new diagnosis has been made, the method proceeds to step506, in which the system provides initial guidelines for intervention selection among a group of available treatment options, and based on prior training of the algorithm for optimal outcomes. Such intervention may be based on novel therapies developed by third parties, which are expected to be developed over time. Thus, the system may be updated on a regular basis to incorporate the current standard of treatment for CD. Thus, the outcomes should continually improve over time. In step507, the system provides guidelines for chronic disease supervision based on algorithm training. Such guidelines may provide short- or long-term follow-up recommendations, goals for exercise, diet, medical treatment, and other advice for successful long-term management of the condition and minimization of secondary complications.

The basis of personalizing the treatment selection is based on results of different patient subpopulations and groups, defined in more detail below. For example, lab results, concurrent diagnoses, and symptom clusters of CD patients tend to differ between adult and pediatric populations. Adults may have unexplained iron-deficiency anemia, fatigue, bone or joint pain, arthritis, osteoporosis or osteopenia (bone loss), liver and biliary tract disorders (transaminitis, fatty liver, primary sclerosing cholangitis, etc.), depression or anxiety, peripheral neuropathy (tingling, numbness or pain in the hands and feet), seizures or migraines, missed menstrual periods, infertility or recurrent miscarriage, canker sores inside the mouth, dermatitis herpetiformis (itchy skin rash). By contrast, pediatric patients may have a clinical picture that focuses more heavily on the gastrointestinal system and developmental issues. Patients may complain of abdominal bloating and pain; chronic diarrhea; vomiting; constipation; pale, foul-smelling, or fatty stools; weight loss; fatigue; irritability and behavioral issues; dental enamel defects of the permanent teeth; signs of malnutrition from lack of nutrient absorption such as delayed growth and puberty, short stature, and failure to thrive; and attention deficit hyperactivity disorder (ADHD).

The disclosed algorithm and system are able, via iterative processing and machine learning, to identify and distinguish between classical and non-classical presentations. In classical celiac disease, patients have signs and symptoms of malabsorption, including diarrhea, steatorrhea (pale, foul-smelling, fatty stools), and weight loss or growth failure in children. In non-classical celiac disease, patients may have mild gastrointestinal symptoms without clear signs of malabsorption or may have seemingly unrelated symptoms. They may suffer from abdominal distension and pain, and/or other indicators such as iron-deficiency anemia, chronic fatigue, chronic migraine, peripheral neuropathy (tingling, numbness or pain in hands or feet), unexplained chronic hypertransaminasemia (elevated liver enzymes), reduced bone mass and bone fractures, and vitamin deficiency (folic acid and B12), late menarche/early menopause and unexplained infertility, dental enamel defects, depression and anxiety, dermatitis herpetiformis (itchy skin rash), and other atypical clinical indicators, which may not be immediately associated with classical CD.

A further ability of the algorithm and system is to identify silent celiac, also known as asymptomatic celiac disease. Such patients are unaware of compromised digestive capacity and do not complain of symptoms, which may be mild, but nevertheless experience damage to their small intestine resulting in villous atrophy. Studies show that despite reporting no symptoms, after going on a strict gluten-free diet these individuals report better health and a reduction in acid reflux, abdominal bloating and distention and flatulence.

Reference is now made toFIG.6, showing a visualization of the embedding space, to illustrate the clustering of subjects with respect to lab values or other exemplary indicators of autoimmune disease. The data illustrate implementation of feature embedding, a machine learning method in which a large multi-dimensional set of features is converted into a smaller dimensional space containing the relevant information of the original data. In this example, feature embedding allows construction of a more efficient and accurate classifier for autoimmune diagnosis that generalizes from the reference population in which diagnoses of autoimmune diseases have been made, to as yet unseen new patient populations. The embedding vector captures semantics of the input by placing semantically similar inputs closer together in the embedding space, as illustrated and described below.

The graph is an output of the T-SNE (t-distributed stochastic neighbor embedding) algorithm, which is a dimensional reduction method that may be used to visualize data set clustering. Specifically, the algorithm takes high-dimensional data and visualizes them in a low-dimensional space of two or three dimensions. In this two-dimensional graph, the x- and y-axes represent transformed parameters that visually represent the similarities and dissimilarities between different inputs or points, each having a mean positioned at zero and deviations extending in both positive and negative directions from the mean. From these representations, it is possible to differentiate the clusters/groups and therefore predict, based on an individual subject's embedded feature vector, if he/she has or is likely to develop, a condition under consideration. The distribution in two dimensions of training data points for the transformed parameters in a given population is represented by black dots, whereas new patient data points are shown in empty dots, as explained further below. The larger, general population with normal values for the measured parameters are shown in the central-lower region of the graph, illustrating a range of normal values for the given parameters. By contrast, individuals diagnosed with a specific disease have values that differ significantly from normal and are part of the distinct clusters601,602, and603outlined by dotted ovals in the upper limits of the graph. These smaller disease clusters represent individuals having values that fall far from the mean average of normal individuals in the general population for the measured parameter on the y-axis, i.e., above the normal threshold. In terms of autoimmune disease, each small cluster may represent, for example, individuals identified as having or being predisposed to develop, CD601, ulcerative colitis602, or Crohn's disease603. Thus, even though all of the individuals in these disease clusters have values outside—in this case, above—the normal threshold for the parameter measured on the y-axis, they vary among each other in terms of the second parameter represented on the x-axis, and each cluster or diagnosis can thus be distinguished from the others. In this example, the individuals in each disease cluster display values for the parameter represented on the x-axis which are below normal601, normal602, or above normal603.

The new subjects' data points, shown as empty dots, appear throughout the parameter range and cluster together with similar subjects from the training set, so the classifier algorithm is able to use the clustering to suggest the correct diagnosis for such patients. The transformation of the feature vectors into the embedding space allows the system to predict or diagnose an individual at risk of a given autoimmune disease by placing this subject close to others with similar parameter values, i.e., sharing the same signs, symptoms, and other diagnostic criteria.

Individuals who have been screened and have a probability of CD diagnosis that is above normal but fails to reach threshold can be monitored with additional visits to follow the course of signs and symptoms over time, to determine whether the threshold is reached that would transfer the individual from the normal group to the treatment group.

Reference is now made toFIG.7, showing a schematic representation of the system structure700used to perform the methods described herewithin above. In this disclosure, the term system may refer to, be part of, or include an Application Specific Integrated Circuit (ASIC); a digital, analog, or mixed analog/digital discrete circuit; a digital, analog, or mixed analog/digital integrated circuit; a combinational logic circuit; a field programmable gate array; at least one processor702(shared, dedicated, or group) that executes code; memory701(shared, dedicated, or group) that stores code executed by a processor702; other suitable hardware components, such as optical, magnetic, or solid state drives, that provide the described functionality; or a combination of some or all of the above, such as in a system-on-chip.

The term code, as used above, may include software, firmware, and/or microcode, and may refer to programs, routines, functions, classes, and/or objects. The term shared processor encompasses a single processor that executes some or all code from multiple modules. The term group processor encompasses a processor that, in combination with additional processors, executes some or all code from one or more modules. The term shared memory encompasses a single memory that stores some or all code from multiple modules. The term group memory encompasses a memory that, in combination with additional memories, stores some or all code from one or more modules. The term memory may be a subset of the term computer-readable medium. The term computer-readable medium does not encompass transitory electrical and electromagnetic signals propagating through a medium, and may therefore be considered tangible and non-transitory. Non-limiting examples of a non-transitory tangible computer readable medium include nonvolatile memory, volatile memory, magnetic storage, and optical storage.

The apparatuses and methods described in this disclosure may be partially or fully implemented by one or more computer programs executed by one or more processors702. The computer programs include processor-executable instructions that are stored on at least one non-transitory tangible computer readable medium, i.e., memory701. The computer programs may also include and/or rely on stored data703,704.

In some implementations, the system comprises a memory701, processors and graphic processing units702, cloud application program interface or storage703, other storage and databases704, and a user interface705. The components of the system700are further delineated below, with reference to the steps of the exemplary method inFIG.1to which they correspond. The memory701may comprise data relating to patient feature vectors706(FIG.1, steps106a,106b), patient diagnosis probability vectors707(FIG.1, step109), and expert medical logic708(FIG.1, step102). The processing unit702may comprise algorithms of artificial intelligence, machine learning, and deep learning709, a controller710, and supervised and self-supervised training and inference711(FIG.1, steps103,104,106a,106b,108a,108b). The cloud storage703may comprise historic population medical data (FIG.1, step101,105). The at least one database704may comprise the data incorporating classifier model parameters715and embedding model parameters716. The user interface705communicates with the medical staff or other professionals using the system, and provides the output of the system, such as a diagnosis or list of possible diagnoses, ranked in order of likelihood712, referrals to specialists and follow-up guidelines713, and in some implementations, treatment recommendations or guidelines714.

In some implementations, the user interface is configured to communicate with other systems and share information via the IoT and other tools. The system may be configured to provide alerts to doctor or to insurer system or even to the subject via health app or other patient interface. Furthermore, the system may be configured to receive feedback from the user or a doctor regarding the accuracy of the classifier model results. Such human feedback regarding diagnosis or treatment/follow-up recommendations may be incorporated in order to influence future training cycles of its models, such as is shown in step110ofFIG.1and408ofFIG.4.

Example embodiments are provided so that this disclosure will be thorough, and will fully convey the scope to those who are skilled in the art. Numerous specific details are set forth such as examples of specific components, devices, and methods, to provide a thorough understanding of embodiments of the present disclosure. It will be apparent to those skilled in the art that specific details need not be employed, that example embodiments may be embodied in many different forms and that neither should be construed to limit the scope of the disclosure.

It is appreciated by persons skilled in the art that the present invention is not limited by what has been particularly shown and described hereinabove. Rather the scope of the present invention includes both combinations and sub-combinations of various features described hereinabove as well as variations and modifications thereto which would occur to a person of skill in the art upon reading the above description and which are not in the prior art.