Machine Learning‐Based Prediction of Prolonged Duration of Mechanical Ventilation Using Medication Data
Medication-related variables contributed incremental value to PMV prediction. ML methods provided marginal improvements over regression models. These findings highlight the potential value of medication data in prediction modeling for patient outcomes but emphasize the need to contextualize the value of complex models over simpler alternatives.
Authors
- Brian Murray (ORCID: https://orcid.org/0000-0001-6660-9749)
- Bokai Zhao
- Xianyan Chen
- Yanlei Kong
- Sheng Li (ORCID: https://orcid.org/0000-0002-6258-3147)
- Ye Shen (ORCID: https://orcid.org/0000-0002-0413-6218)
- Andrea Sikora
- the MRC‐ICU Investigator Team
- Susan E. Smith
- Zhetao Chen
Institutions
- Georgia College & State University (US)
- University of Georgia (US)
- Augusta University (US)
- University of Virginia (US)
- University of Colorado Anschutz Medical Campus (US)
- University of Montana (US)
- Renmin University of China (CN)
- Franklin College (US)
- University of Colorado Denver (US)
Publication Details
- Journal
- Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy
- Published
- 2026-01-06
- DOI
- https://doi.org/10.1002/phar.70090
- Citations
- 1
- Primary Topic
- Respiratory Support and Mechanisms
- Type
- article
- Field-Weighted Citation Impact
- 36.44