Medical decision support for treating infectious diseases

Abstract

With an ever increasing digitalization of health care and records thereof, possibilities of applying data driven methods to analyze and predict healthcare outcomes are becoming more numerous. Markov decision processes (MDPs) are an interesting class of models to explore in this context. Originally born out of the operations research field, they allow us to model sequential decision making under uncertainty. Furthermore they may be extended to partially observable MDPs (POMDPs), in order to model further uncertainty, such as decision making with incomplete information which is often the case in healthcare. In this project, a literature review has been made in order to analyze and discuss mainly POMDPs from the perspective of treating sepsis and COVID-19.