# Model-Based Reinforcement Learning for Sepsis Treatment

arXiv: https://arxiv.org/abs/1811.09602 · HTML: https://mlsanity.com/paper/1811.09602/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2018.

## Built on by (most cited first, 21 of 21)

- [Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounders](https://mlsanity.com/paper/2302.00662/) (2023)
- [A Survey of Safe Reinforcement Learning and Constrained MDPs: A Technical Survey on Single-Agent and Multi-Agent Safety](https://mlsanity.com/paper/2505.17342/) (2025)
- [medDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support](https://mlsanity.com/paper/2505.19785/) (2025)
- [Challenges for Reinforcement Learning in Healthcare](https://mlsanity.com/paper/2103.05612/) (2021)
- [Feature-Based Interpretable Reinforcement Learning based on State-Transition Models](https://mlsanity.com/paper/2105.07099/) (2021)
- [Reinforced Sequential Decision-Making for Sepsis Treatment: The POSNEGDM Framework with Mortality Classifier and Transformer](https://mlsanity.com/paper/2403.07309/) (2024)
- [Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination](https://mlsanity.com/paper/2405.18556/) (2024)
- [Advancing Multi-Organ Disease Care: A Hierarchical Multi-Agent Reinforcement Learning Framework](https://mlsanity.com/paper/2409.04224/) (2024)
- [Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes](https://mlsanity.com/paper/2407.00364/) (2024)
- [Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care](https://mlsanity.com/paper/2306.08044/) (2023)
- [Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment](https://mlsanity.com/paper/2407.19380/) (2024)
- [Off-Policy Evaluation Under Nonignorable Missing Data](https://mlsanity.com/paper/2507.06961/) (2025)
- [Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Model](https://mlsanity.com/paper/2605.14723/) (2026)
- [EHR-MPC: Inference-Time Control for Sepsis Treatment with Generative Patient Digital Twins](https://mlsanity.com/paper/2607.08793/) (2026)
- [A optimization framework for herbal prescription planning based on deep reinforcement learning](https://mlsanity.com/paper/2304.12828/) (2023)
- [Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm](https://mlsanity.com/paper/2409.15753/) (2024)
- [Identifying Differential Patient Care Through Inverse Intent Inference](https://mlsanity.com/paper/2411.07372/) (2024)
- [Deep Attention Q-Network for Personalized Treatment Recommendation](https://mlsanity.com/paper/2307.01519/) (2023)
- [medR: Reward Engineering for Clinical Offline Reinforcement Learning via Tri-Drive Potential Functions](https://mlsanity.com/paper/2602.03305/) (2026)
- [Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness](https://mlsanity.com/paper/2604.21235/) (2026)
- [Evaluating covariate balance for long time horizon Markov decision processes](https://mlsanity.com/paper/2607.15080/) (2026)

## Abstract (arXiv)

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.