Estimation of Risk-Adjusted Outcomes for Non-Infectious Postoperative Complications using Interpretable Machine Learning and Electronic Health Record Data
Risk-adjusted postoperative outcomes estimated using machine learning on EHR data were similar to those produced by manual chart review. These models could be used to augment manual chart review to guide surgical quality improvement.
Authors
- Yaxu Zhuang (ORCID: https://orcid.org/0000-0001-6754-1424)
- Kathryn L. Colborn (ORCID: https://orcid.org/0000-0001-8399-6330)
- Christina M. Stuart (ORCID: https://orcid.org/0000-0002-9851-8486)
- Robert A. Meguid (ORCID: https://orcid.org/0000-0003-2646-4252)
- Michael R. Bronsert (ORCID: https://orcid.org/0000-0003-1879-6193)
- Adam R. Dyas (ORCID: https://orcid.org/0000-0002-2136-8010)
- Yizhou Fei
- William G. Henderson
Institutions
- Colorado School of Public Health (US)
- Outcomes Research Consortium (US)
- University of Colorado Anschutz Medical Campus (US)
Publication Details
- Journal
- Annals of Surgery
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1097/sla.0000000000006737
- Citations
- 2
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 4.96