Implementation of Effective Models for Classifying Medical Data Using Text Mining

Abstract

The paper is devoted to the development and implementation of effective models of medical data classification by text mining for decision support in the diagnosis of pulmonological diseases in children and adolescents of the Altai Territory. Medical data contains important information about patients. Test results are usually retained as structured data, but some data are retained in the form of natural language texts (medical history, the results of physical examination, and the results of other examinations). The paper assesses the quality of the developed methods for extracting information from clinical texts. An assessment of the method for the automatic diagnosis of pulmonological diseases in a test sample is conducted. The most informative features, as well as suitable machine learning methods for classifying patients by disease groups, are identified. Many tasks arising in clinical practice can be automated by applying methods for intelligent analysis of structured and unstructured data that will lead to improvement of the healthcare quality. The results of the research indicate the prospect of using models to support decisionmaking in the primary diagnosis of pulmonological diseases in children and adolescents of the Altai Territory.