Physical activity trends as predictors of postoperative complications in oncology patients: A machine learning approach
This exploratory study showed that postoperative activity trends alone were insufficient to predict complications after major oncological surgery. Combining accelerometer, physiological, and laboratory data may improve predictive accuracy and overall clinical value in perioperative care.
Authors
- Petra Bor (ORCID: https://orcid.org/0000-0002-8494-349X)
- Cindy Veenhof (ORCID: https://orcid.org/0000-0003-0970-2896)
- Jan willem Van de Berg
- Richard P. Meijer (ORCID: https://orcid.org/0000-0003-2510-7982)
- Karin Valkenet (ORCID: https://orcid.org/0000-0002-4715-685X)
- Sjoerd de Vries (ORCID: https://orcid.org/0000-0002-5306-6797)
- Laura Veerhoek
- C. Llorente (ORCID: https://orcid.org/0009-0009-1196-276X)
Institutions
- University of Applied Sciences Utrecht (NL)
- University Medical Center Utrecht (NL)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251408520
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00