Formal Methods, artificial intelligence, big-data analytics, and knowledge engineering in medical care to reduce disease burden and health disparities

Abstract

Medical errors and overtreatment combined with growing non-communicable disease population are responsible for increase in the burden of disease and health disparity. To control this burden and disparity, automation with zero defects must be introduced in evidence based medicine. In safety critical systems, zero defects are achieved through formal methods. A formal model is tested (proved) and the target system is generated through automation with the removal of error prone programming or construction phase. Inspired by similar ideas, we created DocDx, a novel formal method driven medical care framework without any programming phase involved. We convert clinical pathways into a multipartite directed weighted graph (MDWG) that embeds the medical intelligence. The autonomous interpreters in the server presents natural language generator (NLG) pathophysiology questions a doctor would normally ask a patient to understand the signs and symptoms of a disease. The biological terms and human understandable unstructured text entered in DocDx client is made machine understandable through AI NLP engine and translated into biomedical ontology concepts. A new medical condition or presentation of disease in DocDx will need a new clinical pathway translated into a MDWG without the need for any programming or application development process either at the client or at the server end.