Summary of Pruning the Path to Optimal Care: Identifying Systematically Suboptimal Medical Decision-making with Inverse Reinforcement Learning, by Inko Bovenzi et al.
Pruning the Path to Optimal Care: Identifying Systematically Suboptimal Medical Decision-Making with Inverse Reinforcement Learning
by Inko Bovenzi, Adi Carmel, Michael Hu, Rebecca M. Hurwitz, Fiona McBride, Leo Benac, José Roberto Tello Ayala, Finale Doshi-Velez
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantitative Methods (q-bio.QM); Applications (stat.AP); Computation (stat.CO); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel application of Inverse Reinforcement Learning (IRL) is presented, which identifies suboptimal clinician actions in medical decision-making from observational data in clinical settings. The approach involves two stages of IRL with an intermediate step to prune trajectories displaying behavior that deviates significantly from the consensus. This enables effective identification of clinical priorities and values from ICU data containing both optimal and suboptimal clinician decisions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses Inverse Reinforcement Learning (IRL) to help doctors make better decisions by identifying what other doctors are doing well or poorly in an intensive care unit (ICU). It looks at how doctors act and figures out what’s working best. This can help prioritize medical decisions and understand which patients may be affected. |
Keywords
* Artificial intelligence * Reinforcement learning
