Modelling and analysis of complex patient-treatment process using graphminer toolbox

Abstract

This article describes the results of multidisciplinary research in the areas of analysis and modeling of complex processes of treatment on the example of patients with cardiovascular diseases. The aim of this study is to develop tools and methods for the analysis of highly variable processes. In the course of the study, methods and algorithms for processing large volumes of various and semi-structured series data of medical information systems were developed. Moreover, the method for predicting treatment events has been developed. Treatment graph and algorithms of community detection and machine learning method are applied. The use of graphs and machine learning methods has expanded the capabilities of process mining for a better understanding of the complex process of medical care. Moreover, the algorithms for parallel computing using CUDA for graph calculation is developed. The improved methods and algorithms are considered in the corresponding developed visualization tool for complex treatment processes analysis.