A machine learning approach to predicting inpatient mortality among pediatric acute gastroenteritis patients in Kenya
This study demonstrates promising predictive performance of the proposed algorithm for identifying patients at risk of mortality in resource-limited settings. However, further validation in real-world clinical settings is needed to assess its feasibility and potential impact on patient outcomes.
Authors
- Gabriel Otieno Okello (ORCID: https://orcid.org/0000-0002-3866-4756)
- Richard Omore (ORCID: https://orcid.org/0000-0003-3702-3030)
- Vincent Mzazi
- Billy Ogwel (ORCID: https://orcid.org/0000-0002-9097-2713)
- Kirkby D. Tickell (ORCID: https://orcid.org/0000-0002-4108-1236)
- Bryan O. Nyawanda (ORCID: https://orcid.org/0000-0003-0069-1473)
Institutions
- Alliant International University (US)
- University of South Africa (ZA)
- University of Washington (US)
- Kenya Medical Research Institute (KE)
- United States International University Africa (KE)
Publication Details
- Journal
- Learning Health Systems
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1002/lrh2.10478
- Primary Topic
- Viral gastroenteritis research and epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 0.00