This paper is in the following e-collection/theme issue:

Decision Support for Health Professionals (1493) Clinical Information and Decision Making (1845) Design of Processes and Workflows (176) Machine Learning (2008) Respiratory Medicine (147) Electronic Health Records (1283) Artificial Intelligence (2034)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69145, first published .
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

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

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

Authors of this article:

Jung Min Lee1 Author Orcid Image ;   Shengpu Tang1, 2 Author Orcid Image ;   Michael Sjoding3 Author Orcid Image ;   Jenna Wiens1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Jung Min Lee   1 , MS ;   Shengpu Tang   1, 2 , PhD ;   Michael Sjoding   3 , MD ;   Jenna Wiens   1 , PhD

1 Division of Computer Science and Engineering, College of Engineering, University of Michigan, Ann Arbor, MI, United States

2 Department of Computer Science, Emory College of Arts and Sciences, Emory University, Atlanta, GA, United States

3 Division of Pulmonary and Critical Care Medicine, Michigan Medicine, University of Michigan, Ann Arbor, MI, United States

Corresponding Author:

  • Jenna Wiens, PhD
  • Division of Computer Science and Engineering, College of Engineering, University of Michigan
  • 2260 Hayward St
  • Ann Arbor, MI 48109
  • United States
  • Phone: 1 7346474832
  • Email: wiensj@umich.edu