Developing and validating machine learning models to predict length of hospitalization before obese patients undergo elective arthroplasty
All models demonstrated strong predictive capability, underscoring their clinical relevance. These insights can guide preoperative planning, patient counseling, and resource allocation, helping optimize care and discharge strategies.
Authors
- Jose M. Gutierrez-Naranjo
- Michael L. Parks (ORCID: https://orcid.org/0000-0002-3568-6212)
- Alejandro González Della Valle (ORCID: https://orcid.org/0000-0002-2177-5919)
- Aaron I Weinblatt (ORCID: https://orcid.org/0000-0002-0047-8413)
- Felix Conrad Oettl (ORCID: https://orcid.org/0000-0001-9721-685X)
- Fred D. Cushner
Institutions
- Hospital for Special Surgery (US)
- University of Zurich (CH)
- Universitätsklinik Balgrist (CH)
Publication Details
- Journal
- Journal of Orthopaedics
- Published
- 2025-10-17
- DOI
- https://doi.org/10.1016/j.jor.2025.10.008
- Primary Topic
- Total Knee Arthroplasty Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00