Bias in Prediction Models to Identify Patients With Colorectal Cancer at High Risk for Readmission After Resection
Predictive models for 30-day readmission after colorectal surgery may perform unequally for different race groups, potentially propagating to inequalities in delivery of care and patient outcomes if the predictions from these models are used to direct care.
Authors
- Jonathan Laryea (ORCID: https://orcid.org/0000-0001-6550-9252)
- Mario Schootman (ORCID: https://orcid.org/0000-0003-1162-8824)
- Sonia T. Orcutt (ORCID: https://orcid.org/0000-0003-2611-2556)
- Mary M. Lucas (ORCID: https://orcid.org/0000-0002-0413-7499)
- Jennifer A. Rumpel (ORCID: https://orcid.org/0000-0002-5883-218X)
- Christopher C. Yang (ORCID: https://orcid.org/0000-0001-5463-6926)
- Chenghui Li (ORCID: https://orcid.org/0000-0002-2778-5149)
- Jun Ying (ORCID: https://orcid.org/0000-0002-7693-4243)
Institutions
- Winthrop Rockefeller Foundation (US)
- Drexel University (US)
- University of Arkansas for Medical Sciences (US)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1200/cci.23.00194
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- National Science Foundation
- U.S. Department of Defense