Medical decision support for treating infectious diseases

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/300810
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Type: Examensarbete för masterexamen
Title: Medical decision support for treating infectious diseases
Authors: Ölund, Hugo
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.
Keywords: Partially observable Markov decision processes, Bayesian reinforcement learning
Issue Date: 2020
Publisher: Chalmers tekniska högskola / Institutionen för matematiska vetenskaper
URI: https://hdl.handle.net/20.500.12380/300810
Collection:Examensarbeten för masterexamen // Master Theses



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