Risk health evaluation and selection of preventive measures plan with the help of argumental algorithm

Abstract

The paper describes knowledge base principles and method for disease risk evaluation that were used for intelligent healthcare management system creation. The present version of the knowledge base is implemented using a heterogeneous semantic network approach and utilizes expert opinions about risk factors and events influencing an individual’s health. Data includes genetic predisposition, lifestyle, and external environment. Data is compiled with the aid of questionnaires, mobile devices, case histories and information from social media. Information from social media is analyzed using data and text mining methods with the goal of evaluating the user’s condition. All of the data obtained is accumulated in a single database. The method for risk evaluation and preventive measures plan hypotheses generation is based on an argumentation reasoning algorithm that is modified to the task at hand. All prevention recommendations are based on the principles of P4 medicine. The current version of the system is based on expert knowledge obtained by automated monitoring and analysis of a large number of publications and recommendations on this topic.