Development, validation and recalibration of a prediction model for prediabetes: an EHR and NHANES-based study
The approximation method demonstrated comparable performance to LASSO in the EHR development cohort, making it a viable option for healthcare organizations with limited resources to collect a comprehensive set of candidate predictor variables. NHANES data may be suitable for externally validating a clinical prediction model developed with EHR data to assess generalizability to a nationally representative sample, depending on the model's intended use and the alignment of predictor variable definitions with those used in the model's original development.
Authors
- Kristin M. Lenoir (ORCID: https://orcid.org/0000-0003-1834-7398)
- Brian J. Wells (ORCID: https://orcid.org/0000-0001-7310-6525)
- Joseph Rigdon (ORCID: https://orcid.org/0000-0001-6265-0752)
- Nicholas J. Casacchia (ORCID: https://orcid.org/0000-0003-2291-835X)
Institutions
- Cleveland Clinic (US)
- Community Care (US)
- Wake Forest University (US)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12911-024-02803-w
- Citations
- 2
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.69
Funders
- National Institutes of Health
- National Center for Advancing Translational Sciences
- Wake Forest Clinical and Translational Science Institute, Wake Forest School of Medicine