# How Consistent are Clinicians? Evaluating the Predictability of Sepsis Disease Progression with Dynamics Models

arXiv: https://arxiv.org/abs/2404.07148 · HTML: https://mlsanity.com/paper/2404.07148/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 10 Apr 2024.

## Placed in

- Deep reinforcement learning methods / [RL for clinical treatment and surgery](https://mlsanity.com/deeprl/rl-for-clinical-treatment-and-surgery/)

## Builds on (most cited first, 9 of 9)

- [Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1705.08422/) (2017)
- [Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare](https://mlsanity.com/paper/2305.01738/) (2023)
- [Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions](https://mlsanity.com/paper/2002.03478/) (2020)
- [Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning](https://mlsanity.com/paper/1901.04670/) (2019)
- [An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare](https://mlsanity.com/paper/2011.11235/) (2020)
- [Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment](https://mlsanity.com/paper/2101.08477/) (2021)
- [Does the "Artificial Intelligence Clinician" learn optimal treatment strategies for sepsis in intensive care?](https://mlsanity.com/paper/1902.03271/) (2019)
- [Offline reinforcement learning with uncertainty for treatment strategies in sepsis](https://mlsanity.com/paper/2107.04491/) (2021)
- [Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies](https://mlsanity.com/paper/2010.04279/) (2020)

## Built on by (most cited first, 2 of 2)

- [Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC](https://mlsanity.com/paper/2602.14505/) (2026)
- [Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions](https://mlsanity.com/paper/2602.00259/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) is a promising approach to generate treatment policies for sepsis patients in intensive care. While retrospective evaluation metrics show decreased mortality when these policies are followed, studies with clinicians suggest their recommendations are often spurious. We propose that these shortcomings may be due to lack of diversity in observed actions and outcomes in the training data, and we construct experiments to investigate the feasibility of predicting sepsis disease severity changes due to clinician actions. Preliminary results suggest incorporating action information does not significantly improve model performance, indicating that clinician actions may not be sufficiently variable to yield measurable effects on disease progression. We discuss the implications of these findings for optimizing sepsis treatment.