Trustworthiness of a machine learning early warning model in medical and surgical inpatients
Characterization of the target patient subpopulations has clinical implications and should be considered when developing models to be used in general hospital wards.
Authors
- Genevieve B. Melton (ORCID: https://orcid.org/0000-0001-5193-1663)
- Hojjat Salehinejad (ORCID: https://orcid.org/0000-0002-9636-863X)
- Pedro J. Caraballo (ORCID: https://orcid.org/0000-0001-9050-7080)
- György Simon (ORCID: https://orcid.org/0000-0003-4715-5934)
- Bijan J. Borah (ORCID: https://orcid.org/0000-0002-4526-462X)
- Anne M. Meehan
- Parvez Rahman
- Karen M. Fischer (ORCID: https://orcid.org/0000-0001-5037-6100)
Institutions
- University of Minnesota (US)
- University of Minnesota System (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- JAMIA Open
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1093/jamiaopen/ooae156
- Citations
- 3
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.16
Funders
- University of Minnesota