ArticleNPJ digital medicine2022
An interpretable RL framework for pre-deployment modeling in ICU hypotension management.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Data-driven decision making in patient management: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Reinforcement Learning for Intraoperative Hypotension Management with Consideration to Postoperative Acute Kidney Injury.Kidney360 · 2026Article
- Real-time, artificial intelligence-guided intraoperative resuscitation and fluid management in trauma anesthesia.Current opinion in anaesthesiology · 2026Review
- Liver transplant donor-recipient matching with offline reinforcement learning.NPJ digital medicine · 2026Article
- Artificial intelligence in haematology laboratory diagnosis: Current applications, challenges, and future directions.African journal of laboratory medicine · 2026Review
- Optimizing Loop Diuretic Treatment for Mortality Reduction in Patients With Acute Dyspnea Using a Practical Offline Reinforcement Learning Pipeline for Health Care: Retrospective Single-Center Simulation Study.JMIR medical informatics · 2025Article
- Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning.ArXiv · 2025Article
- Personalized decision making for coronary artery disease treatment using offline reinforcement learning.NPJ digital medicine · 2025Article
- Safety of human-AI cooperative decision-making within intensive care: A physical simulation study.PLOS digital health · 2025Article
- Reinforcement learning model for optimizing dexmedetomidine dosing to prevent delirium in critically ill patients.NPJ digital medicine · 2024Article
- Development and validation of a reinforcement learning model for ventilation control during emergence from general anesthesia.NPJ digital medicine · 2023Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
Abstract
Computational methods from reinforcement learning have shown promise in inferring treatment strategies for hypotension management and other clinical decision-making challenges. Unfortunately, the resulting models are often difficult for clinicians to interpret, making clinical inspection and validation of these computationally derived strategies challenging in advance of deployment. In this work, we develop a general framework for identifying succinct sets of clinical contexts in which clinicians make very different treatment choices, tracing the effects of those choices, and inferring a set of recommendations for those specific contexts. By focusing on these few key decision points, our framework produces succinct, interpretable treatment strategies that can each be easily visualized and verified by clinical experts. This interrogation process allows clinicians to leverage the model's use of historical data in tandem with their own expertise to determine which recommendations are worth investigating further e.g. at the bedside. We demonstrate the value of this approach via application to hypotension management in the ICU, an area with critical implications for patient outcomes that lacks data-driven individualized treatment strategies; that said, our framework has broad implications on how to use computational methods to assist with decision-making challenges on a wide range of clinical domains.
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What 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.