Peer-Reviewed Publication
Health Sci Rep2026;9(8):e72956.August 1, 2026Journal Article
Predicting Outcomes of Traumatic Brain Injury Using Machine Learning Models Among Patients at Kilimanjaro Christian Medical Centre, Tanzania: A Registry-Based Cohort Study.
William Nkenguye1,2,3, Edwin Joseph Shewiyo1,2,3, João Vitor Perez De Souza3,4, Timothy Antipas Peter1,2,3, Alice Andongolile1,2,3, Rosalia Njau1,2,3, Baraka Moshi1,2,3, Johnston George1, Paige O'Leary3,4,5, Catherine Staton3,4,5, Blandina T Mmbaga2,3,5,6, Francis M Sakita2,3,6, João Ricardo Nickenig Vissoci3,4,5, Innocent B Mboya1,7
1Department of Epidemiology and Biostatistics, School of Public Health KCMC University Moshi Tanzania.
2Kilimanjaro Clinical Research Institute Moshi Tanzania.
3Global Emergency Medicine Innovation and Implementation (GEMINI) Research Centre, Duke Global Health Institute Duke University Durham North Carolina USA.
4Duke Department of Emergency Medicine Duke University Medical Center Durham North Carolina USA.
5Duke Global Health Institute Duke University Durham North Carolina USA.
6Kilimanjaro Christian Medical Center Moshi Tanzania.
7Africa Academy for Public Health Dar es Salaam Tanzania.
Abstract
BACKGROUND: Traumatic brain injury (TBI) remains a major global health burden, disproportionately affecting low- and middle-income countries (LMICs) where access to neurocritical care is limited. Accurate and context-appropriate prognostic models are crucial to guide early clinical decision-making and optimize resource allocation in such settings. This study aims to develop and evaluate machine le…
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