A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategies

TL;DR

This paper introduces a Conservative Q-Learning approach to address distribution shift issues in offline reinforcement learning for sepsis treatment, aiming to improve decision-making and patient outcomes.

Contribution

The study applies Conservative Q-Learning to mitigate action distribution shift in offline RL for sepsis treatment, resulting in policies closer to physician practices.

Findings

CQL policy aligns more closely with physician actions

Improved treatment decision quality in ICU settings

Potential to enhance patient survival rates

Abstract

Sepsis is a leading cause of mortality and its treatment is very expensive. Sepsis treatment is also very challenging because there is no consensus on what interventions work best and different patients respond very differently to the same treatment. Deep Reinforcement Learning methods can be used to come up with optimal policies for treatment strategies mirroring physician actions. In the healthcare scenario, the available data is mostly collected offline with no interaction with the environment, which necessitates the use of offline RL techniques. The Offline RL paradigm suffers from action distribution shifts which in turn negatively affects learning an optimal policy for the treatment. In this work, a Conservative-Q Learning (CQL) algorithm is used to mitigate this shift and its corresponding policy reaches closer to the physicians policy than conventional deep Q Learning. The policy learned could help clinicians in Intensive Care Units to make better decisions while treating septic patients and improve survival rate.