Domain-Aware Interpretable Machine Learning Model for Predicting Postoperative Hospital Length of Stay from Perioperative Data: A Retrospective Observational Cohort Study
Interpretable machine learning enables accurate and generalizable estimation of postoperative LOS while revealing clinically actionable perioperative domains. Such frameworks may facilitate more efficient perioperative planning, improved allocation of hospital resources, and personalized recovery strategies.
Authors
- Joseph R. Scarpa
- Iqram Hussain
- Richard Boyer
Institutions
- Cornell University (US)
Publication Details
- Journal
- Bioengineering
- Published
- 2026-01-27
- DOI
- https://doi.org/10.3390/bioengineering13020147
- Primary Topic
- Enhanced Recovery After Surgery
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Foundation for Anesthesia Education and Research
- National Institutes of Health
- National Institute on Aging