# Reinforced Sequential Decision-Making for Sepsis Treatment: The POSNEGDM Framework with Mortality Classifier and Transformer

arXiv: https://arxiv.org/abs/2403.07309 · HTML: https://mlsanity.com/paper/2403.07309/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems](https://mlsanity.com/paper/2203.01387/) (2022)
- [Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1705.08422/) (2017)
- [Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning](https://mlsanity.com/paper/1901.04670/) (2019)
- [Model-Based Reinforcement Learning for Sepsis Treatment](https://mlsanity.com/paper/1811.09602/) (2018)
- [Invariant Causal Imitation Learning for Generalizable Policies](https://mlsanity.com/paper/2311.01489/) (2023)
- [Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learning](https://mlsanity.com/paper/2010.04767/) (2020)
- [A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategies](https://mlsanity.com/paper/2203.13884/) (2022)
- [Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space](https://mlsanity.com/paper/2206.11190/) (2022)

## Built on by (most cited first, 6 of 6)

- [A Sharper Global Convergence Analysis for Average Reward Reinforcement Learning via an Actor-Critic Approach](https://mlsanity.com/paper/2407.18878/) (2024)
- [Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach](https://mlsanity.com/paper/2501.16243/) (2025)
- [Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC](https://mlsanity.com/paper/2602.14505/) (2026)
- [Global Convergence of Average Reward Constrained MDPs with Neural Critic and General Policy Parameterization](https://mlsanity.com/paper/2603.07698/) (2026)
- [Exploring a Graph-based Approach to Offline Reinforcement Learning for Sepsis Treatment](https://mlsanity.com/paper/2509.03393/) (2025)
- [Hierarchical Multilevel Monte Carlo for Order-Optimal Neural Actor-Critic in Average-Reward CMDPs](https://mlsanity.com/paper/2607.28390/) (2026)

## Abstract (arXiv)

Sepsis, a life-threatening condition triggered by the body's exaggerated response to infection, demands urgent intervention to prevent severe complications. Existing machine learning methods for managing sepsis struggle in offline scenarios, exhibiting suboptimal performance with survival rates below 50%. This paper introduces the POSNEGDM -- ``Reinforcement Learning with Positive and Negative Demonstrations for Sequential Decision-Making" framework utilizing an innovative transformer-based model and a feedback reinforcer to replicate expert actions while considering individual patient characteristics. A mortality classifier with 96.7% accuracy guides treatment decisions towards positive outcomes. The POSNEGDM framework significantly improves patient survival, saving 97.39% of patients, outperforming established machine learning algorithms (Decision Transformer and Behavioral Cloning) with survival rates of 33.4% and 43.5%, respectively. Additionally, ablation studies underscore the critical role of the transformer-based decision maker and the integration of a mortality classifier in enhancing overall survival rates. In summary, our proposed approach presents a promising avenue for enhancing sepsis treatment outcomes, contributing to improved patient care and reduced healthcare costs.