Machine-learning based query construction and pattern identification for hereditary angioedema

Abstract

A method, computer program product, and system identifying a probability of a medical condition in a patient. The method includes a processor obtaining data set(s) related to a patient population diagnosed with a medical condition and based on a frequency of features in the data set(s), identifying common features and weighting the common features based on frequency of occurrence in the data set(s) to generate mutual information. The processor generates pattern(s) including a portion of the common features to generate a machine learning algorithm(s). The processor compiles a training set of data to use to tune the machine learning algorithm(s). The processor dynamically adjusts common features in the pattern(s) such that the machine learning algorithm(s) can distinguish patient data indicating the medical condition from patient data not indicating the medical condition. The processor applies the machine learning algorithm(s) to data related to the undiagnosed patient, to determine the probability.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application is a continuation of U.S. Non-Provisional Ser. No. 15 / 724,480, filed Oct. 4, 2017, entitled, “MACHINE-LEARNING BASED QUERY CONSTRUCTION PATTERN AND IDENTIFICATION FOR HEREDITARY ANGIOEDEMA” which claims priority to U.S. Provisional Application No. 62 / 404,338 filed Oct. 5, 2016, entitled, “MACHINE-LEARNING BASED QUERY CONSTRUCTION AND PATTERN IDENTIFICATION” which is incorporated herein by reference in its entirety.FIELD OF INVENTION[0002]The invention relates to the creation and utilization of machine-based learning algorithms to establish and identify data patterns in the absence of established knowledge regarding these patterns.BACKGROUND OF INVENTION[0003]Health patterns indicative of certain health conditions are often difficult to identify. This is true for diseases and medical conditions that are readily known to the general population, as well as with diseases that are so rare that they affect only a small portion ...

AI Technical Summary

Benefits of technology

[0008]Shortcomings of the prior art are also overcome and additional advantages are provided through the provision of a method determining a probability of the presence of a given medical condition based on a data set related to a patient, the method includes: obtaining, by one or more processors in a distributed computing environment, one or more machine-readable data sets related to a patient population from one or more databases; identifying, by the one or more processors, based on an initial patient definition, a portion of data from the machine-readable data sets related to a patient population, wherein the portion of the data comprises patients of the patient population with a medical condition; based on a frequency of features in the portion of the data, identifying, by the one or more processors, common features in the portion of the data and weighting the common features based on frequency of occurrence in the portion of the data, wherein the common features comprise mutual information; generating, by the one or more processors, one or more patterns comprising a portion of the common features; generating, by the one or more processors, one or more machine learning algorithms based on the one or more patterns, the one or more machine learning algorithms to identify presence or absence of the given medical condition in an undiagnosed patient based on absence or presence of features comprising the one or more patterns in data related to the undiagnosed patient; utilizing, by the one or more processors, statistical sampling to compile a training set of data, wherein the training set comprises data from the one or more data sets and at least one additional data set comprising data related to a population without the medical condition, and wherein utilizing the statistical sampling comprises formulating and obtaining queries based on

Problems solved by technology

Health patterns indicative of certain health conditions are often difficult to identify.

This prolonged diagnostic time can be detrimental as it delays initiating approved treatments and the progression of the disease for an undiagnosed patient may preclude that patient, when finally diagnosed, from enrolling in a clinical trial and/or a given therapy not having any effect, since the disease may have progressed to a state where the therapy is no longer effective.

Most of these diseases are genetic, frequently misdiagnosed for years, and without FDA-approved drug treatment.

The problem of finding potentially undiagnosed subjects for orphan diseases is that active surveillance for such conditions (canvassing a segment of population with questionnaires and/or tests) is expensive and impractical for rare (or even not so rare) diseases, and passive surveillance has to rely on existing medical records (produced by hospitals and insurance companies), which may be incomplete, unreliable,

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