Pruning the Path to Optimal Care: Identifying Systematically Suboptimal Medical Decision-Making with Inverse Reinforcement Learning
On the map: Offline RL and imitation learning from data → Safe offline RL
Abstract (arXiv)
In aims to uncover insights into medical decision-making embedded within observational data from clinical settings, we present a novel application of Inverse Reinforcement Learning (IRL) that identifies suboptimal clinician actions based on the actions of their peers. This approach centers two stages of IRL with an intermediate step to prune trajectories displaying behavior that deviates significantly from the consensus. This enables us to effectively identify clinical priorities and values from ICU data containing both optimal and suboptimal clinician decisions. We observe that the benefits of removing suboptimal actions vary by disease and differentially impact certain demographic groups.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Safe offline RL, one of 13 directions in Offline RL and imitation learning from data. First posted on arXiv 7 Nov 2024.
Builds on
0 of 0 references.
No references here yet.
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2026Quantifying Potential Observation Missingness in Inverse Reinforcement Learning
- 2024Identifying Differential Patient Care Through Inverse Intent Inference
- 2024Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare
- 2019Inverse Reinforcement Learning in Contextual MDPs
- 2023Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning
- 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
- 2023Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care
- 2025Benchmarking Offline Multi-Objective Reinforcement Learning in Critical Care