Model-Based Reinforcement Learning for Sepsis Treatment

By Aniruddh Raghu, Matthieu Komorowski, Sumeetpal S. Singh. 2018

60 citations recorded by the source corpus.

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.

Subfields: Cardiology and Cardiovascular Medicine, Epidemiology, Surgery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu

In Epidemiology

1. Chao Yu
2. Jiming Liu
3. Shamim Nemati
4. Guoqi Ren

In Surgery

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, background): “[199] PS N/A N/A MIMIC-III Model-based learning with continuous state-space; integrating clinician’s policies into RL policies.”