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

The present invention provides a method for determining the likelihood of a medical condition in an undiagnosed patient by analyzing data related to the patient. The method involves identifying common features in the data, weighting them based on their frequency of occurrence, and generating patterns based on these features. These patterns are then used to train machine learning algorithms that can identify the presence or absence of the medical condition in the patient. The method also includes using statistical sampling to form a training set of data and distributing the queries over a group of computing resources to maximize efficiency. The common features are also dynamically adjusted to improve accuracy. The invention provides a computer system and computer program product related to this method.

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, and not contain enough information relevant for the predictive diagnostics.

Challenges in identifying these orphan diseases from population-related data exist based on both the limitations of present computing solutions to process the volume of data efficiently and the lack of knowledge regarding what parameters should be searched within this large volume.

However, during attacks, patients often suffer excruciating abdominal pain, nausea, and vomiting caused by swelling in the intestinal wall.

Swelling of the airway or throat is particularly dangerous, because it can cause death by asphyxiation.

Presently, there is a diagnostic test for the disease, but because HAE it is typically misdiagnosed, it is not typically requested by the provider for years prior to diagnosis.

The challenges related to establishing patterns that identify an event in a large volume of data and actually identifying that event in this large volume are not unique to disease or to orphan disease identification.

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