mlsanityData to 7 Oct 2026

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

arXiv 2403.07309 ↗First posted 12 Mar 2024cs.LG cs.AI cs.CYcited by 6

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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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 15 of its 32 references that are in the map. First posted on arXiv 12 Mar 2024.

Builds on

15 of 32 references.

References · 15

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2017Proximal Policy Optimization Algorithmscited by 14,441
  5. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  6. 2016Generative Adversarial Imitation Learningcited by 1,576
  7. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,528
  8. 2022A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problemscited by 228
  9. 2017Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approachcited by 68
  10. 2019Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learningcited by 34
  11. 2018Model-Based Reinforcement Learning for Sepsis Treatmentcited by 21
  12. 2023Invariant Causal Imitation Learning for Generalizable Policiescited by 15
3 more
  1. 2020Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learningcited by 11
  2. 2022A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategiescited by 5
  3. 2022Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Spacecited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 3.

6 papers.

  1. 2024A Sharper Global Convergence Analysis for Average Reward Reinforcement Learning via an Actor-Critic Approachcited by 14
  2. 2025Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approachcited by 4
  3. 2026Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MCcited by 3
  4. 2026Global Convergence of Average Reward Constrained MDPs with Neural Critic and General Policy Parameterizationcited by 1
  5. 2025Exploring a Graph-based Approach to Offline Reinforcement Learning for Sepsis Treatmentcited by 0
  6. 2026Hierarchical Multilevel Monte Carlo for Order-Optimal Neural Actor-Critic in Average-Reward CMDPscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment
  2. 2021Offline reinforcement learning with uncertainty for treatment strategies in sepsis
  3. 2026Attention-Based Offline Reinforcement Learning and Clustering for Interpretable Sepsis Treatment
  4. 2026Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Model
  5. 2024Identifying Differential Patient Care Through Inverse Intent Inference
  6. 2026Offline Reinforcement Learning for Hemodynamic Management of Sepsis in the ICU: a MIMIC-IV Study with Dual Off-Policy Evaluation
  7. 2026Learning Preference-Based Objectives from Clinical Narratives for Dynamic Sepsis Treatment
  8. 2025MORE-CLEAR: Multimodal Offline Reinforcement learning for Clinical notes Leveraged Enhanced State Representation