Machine Learning Algorithms Exceed Comorbidity Indices in Prediction of Short-Term Complications After Hip Fracture Surgery
ML algorithms offer an improved method to holistically calculate preoperative risk of patient morbidity, mortality, and discharge destination. Through continued validation, risk calculators using these algorithms may inform medical decision making to providers and payers.
Authors
- Edward C. Beck (ORCID: https://orcid.org/0000-0002-5092-0679)
- Joseph N. Liu (ORCID: https://orcid.org/0000-0002-3801-8885)
- Milton T. M. Little (ORCID: https://orcid.org/0000-0002-4317-6378)
- Ryan C. Godwin (ORCID: https://orcid.org/0000-0001-6590-3970)
- Dev M. Patel
- Avinesh Agarwalla (ORCID: https://orcid.org/0000-0001-5056-6780)
- Anirudh K. Gowd (ORCID: https://orcid.org/0000-0001-7151-6459)
- Brian Robert Waterman (ORCID: https://orcid.org/0000-0002-1442-212X)
Institutions
- Cedars-Sinai Medical Center (US)
- Orthopaedic Trauma Association (US)
Publication Details
- Journal
- Journal of the American Academy of Orthopaedic Surgeons
- Published
- 2024-11-19
- DOI
- https://doi.org/10.5435/jaaos-d-23-01144
- Citations
- 3
- Primary Topic
- Hip and Femur Fractures
- Type
- article
- Field-Weighted Citation Impact
- 1.72
Funders
- Orthopaedic Trauma Association