ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
Interpretable Batch IRL to Extract Clinician Goals in ICU Hypotension Management.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The trial behind it
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Who cites it
3 citing papers in PubMed.
- State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review.JMIR medical informatics · 2022Review
- Deep reinforcement learning approaches for global public health strategies for COVID-19 pandemic.PloS one · 2021Article
- Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational ScienceArticle
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Exposing and understanding the motivations of clinicians is an important step for building robust assistive agents as well as improving care. In this work, we focus on understanding the motivations for clinicians managing hypotension in the ICU. We model the ICU interventions as a batch, sequential decision making problem and develop a novel interpretable batch variant of Adversarial Inverse Reinforcement Learning algorithm that not only learns rewards which induce treatment policies similar to clinical treatments, but also ensure that the learned functional form of rewards is consistent with the decision mechanisms of clinicians in the ICU. We apply our approach to understanding vasopressor and IVfluid administration in the ICU and posit that this interpretability enables inspection and validation of the rewards robustly.
Identifiers
32477686PMC7233064What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.