PROGRAM PROCEDURES FOR TRAINING A RECOGNITION SYSTEM FOR THE DIFFERENTIAL DIAGNOSIS OF PATIENTS BASED ON HETEROGENEOUS SYMPTOM COMPLEXES

Abstract

The machine learning recognition system for the differential diagnosis of patients based on heterogeneous nephrology parameter complexes is being considered, transitioning from instrumental means of examination. Training utilizes empirical statistics of clinical cases in a database with reliable diagnoses. The purpose is to expand the capabilities of information extraction from similar databases for training recognition procedures by enriching this toolkit with new features containing characteristic aspects of the extracted information. The research object is the mathematical and software toolkit for training recognition procedures of patient differential diagnosis based on statistics of reliably diagnosed clinical cases. The subject of the study is the software procedures for forming models of parameter complex incidence during training along scales of their values and the procedures for using these models in diagnostics. Model acquisition is perceived as the main content of the training process in ensuring diagnosis differentiation. A criterion for accepting preferential diagnostic decisions using such models is proposed. To simplify the development of mathematical and software procedures, heterogeneous symptom complexes are normalized and transformed to the [0; 1] scale. The introduction states the significant prevalence in medicine and related fields of databases with medical and biomedical data statistics on parameters and characteristics of human organs and systems in different conditions, their medical interpretation, and their use for various purposes, often associated with patient diagnostics. The problems of their formation and use are outlined on real databases, with one complicating factor in the development of diagnostic hardware-software being the substantial heterogeneity of parameters determined by patient examination instruments.