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
Bibliographic record
Abstract
Background: Offline reinforcement learning (RL) has been increasingly applied to clinical decision-making problems. However, due to the lack of a standardized pipeline, prior work often relied on strategies that may lead to overfitted policies and inaccurate evaluations. Objective: In this work, we present a practical pipeline-Pipeline for Learning Robust Policies in Reinforcement Learning (PROP-RL)-designed to improve robustness and minimize disruption to clinical workflow. We demonstrate its efficacy in the context of learning treatment policies for administering loop diuretics in hospitalized patients. Methods: Our cohort included adult inpatients admitted to the emergency department at Michigan Medicine between 2015 and 2019 who required supplemental oxygen. We modeled the management of loop diuretics as an offline RL problem using a discrete state space based on features extracted from electronic health records, a binary action space corresponding to the daily use of loop diuretics, and a reward function based on in-hospital mortality. The policy was trained on data from 2015 to 2018 and evaluated on a held-out set of hospitalizations from 2019, in terms of estimated reduction in mortality compared to clinician behavior. Results: The final study cohort included 36,570 hospitalizations. The learned treatment policy was based on 60 states: the policy deferred to clinicians in 36 states, recommended the majority action in 22 states, and diverged significantly from clinician behavior in 2 of the states. Among the cases where the policy meaningfully diverged from the behavior policy, the learned policy was estimated to significantly reduce the mortality rate from 3.8% to 2.2% by 1.6% (95% CI 0.4-2.7; P=.006). Conclusions: We applied our pipeline to the clinical problem of loop diuretic treatment, highlighting the importance of robust state representation and thoughtful policy selection and evaluation. Our work reveals areas of potential improvement in current clinical care for loop diuretics and serves as a blueprint for using offline RL for sequential treatment selection in clinical settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".