# Deep Reinforcement Learning for Sepsis Treatment

arXiv: https://arxiv.org/abs/1711.09602 · HTML: https://mlsanity.com/paper/1711.09602/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2017.

## Built on by (most cited first, 30 of 71)

- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings](https://mlsanity.com/paper/2107.11003/) (2021)
- [The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms](https://mlsanity.com/paper/2203.06369/) (2022)
- [Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications](https://mlsanity.com/paper/2302.07549/) (2023)
- [Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment](https://mlsanity.com/paper/2101.08477/) (2021)
- [Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning](https://mlsanity.com/paper/2210.02552/) (2022)
- [Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounders](https://mlsanity.com/paper/2302.00662/) (2023)
- [Finding Counterfactually Optimal Action Sequences in Continuous State Spaces](https://mlsanity.com/paper/2306.03929/) (2023)
- [Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidance](https://mlsanity.com/paper/2309.01448/) (2023)
- [Inverse Reinforcement Learning in Contextual MDPs](https://mlsanity.com/paper/1905.09710/) (2019)
- [A Survey of Demonstration Learning](https://mlsanity.com/paper/2303.11191/) (2023)
- [Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models](https://mlsanity.com/paper/2303.12281/) (2023)
- [medDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support](https://mlsanity.com/paper/2505.19785/) (2025)
- [On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning](https://mlsanity.com/paper/2306.05637/) (2023)
- [Challenges for Reinforcement Learning in Healthcare](https://mlsanity.com/paper/2103.05612/) (2021)
- [Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach](https://mlsanity.com/paper/2210.14420/) (2022)
- [Quasi-optimal Reinforcement Learning with Continuous Actions](https://mlsanity.com/paper/2301.08940/) (2023)
- [Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding](https://mlsanity.com/paper/2306.01157/) (2023)
- [Off-policy Evaluation in Doubly Inhomogeneous Environments](https://mlsanity.com/paper/2306.08719/) (2023)
- [Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learning](https://mlsanity.com/paper/2312.10385/) (2023)
- [SPRINQL: Sub-optimal Demonstrations driven Offline Imitation Learning](https://mlsanity.com/paper/2402.13147/) (2024)
- [Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions](https://mlsanity.com/paper/2203.02605/) (2022)
- [ICU-Sepsis: A Benchmark MDP Built from Real Medical Data](https://mlsanity.com/paper/2406.05646/) (2024)
- [A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategies](https://mlsanity.com/paper/2203.13884/) (2022)
- [Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes](https://mlsanity.com/paper/2205.09852/) (2022)
- [HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare](https://mlsanity.com/paper/2302.09212/) (2023)
- [Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination](https://mlsanity.com/paper/2405.18556/) (2024)
- [Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicine](https://mlsanity.com/paper/2204.07124/) (2022)
- [Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space](https://mlsanity.com/paper/2206.11190/) (2022)
- [Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2405.03082/) (2024)

## Abstract (arXiv)

Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions annually. Treating a septic patient 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 propose an approach to deduce treatment policies for septic patients by using continuous state-space models and deep reinforcement learning. Our model learns clinically interpretable treatment policies, similar in important aspects to the treatment policies of physicians. The learned policies could be used to aid intensive care clinicians in medical decision making and improve the likelihood of patient survival.