Model-Based Reinforcement Learning for Sepsis Treatment

Sepsis is a dangerous condition that is a leading cause of patient mortality. Treating sepsis is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we explore the use of continu...

Full description

Saved in:
Bibliographic Details
Main Authors Raghu, Aniruddh, Komorowski, Matthieu, Singh, Sumeetpal
Format Journal Article
LanguageEnglish
Published 23.11.2018
Subjects
Computer Science - Artificial Intelligence
Computer Science - Learning
Statistics - Machine Learning
Online AccessGet full text

Cover

More Information
Summary:Sepsis is a dangerous condition that is a leading cause of patient mortality. Treating sepsis is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we explore the use of continuous state-space model-based reinforcement learning (RL) to discover high-quality treatment policies for sepsis patients. Our quantitative evaluation reveals that by blending the treatment strategy discovered with RL with what clinicians follow, we can obtain improved policies, potentially allowing for better medical treatment for sepsis.
Bibliography:Report number: ML4H/2018/41
DOI:10.48550/arxiv.1811.09602