Developing a Machine Learning Based Clinical Decision-Making Tool for Traumatic Brain Injury Patients in Moshi, Tanzania
| dc.contributor.advisor | Staton, Catherine Lynch | |
| dc.contributor.author | Huo, Lily | |
| dc.date.accessioned | 2023-06-08T18:33:43Z | |
| dc.date.issued | 2023 | |
| dc.department | Global Health | |
| dc.description.abstract | Background: Traumatic brain injury (TBI) has a disproportionate burden on low- and middle-income countries (LMICs) and cost-effective and culturally relevant measures are necessary to improve TBI care. This study aims to characterize emergency healthcare providers’ decision making when treating TBI patients, develop a machine learning-based model to predict TBI patient outcome, and conduct a decision curve analysis (DCA) to evaluate model clinical applicability. Methods: This study is twofold: 1) a secondary analysis of a TBI data registry with 4142 patients and 2) a survey examining physicians decision-making in treating 50 TBI patients in real time. Results: Five machine learning models were developed with AUCs ranging from 70.86% (Single C5.0 Ruleset) to 85.67% (Ensemble Model). DCA showed that all models exhibited a greater net benefit over ranges of clinical thresholds. The survey collected information on 50 patients providing insight on tools used by physicians in real-time when treating TBI patients as well as the unmet need patients at KCMC faced. Conclusions: This study is the first to use machine learning modeling and DCA in the context of TBI prognosis in Sub-Saharan Africa. Prognostic models have great potential within the decision-making process for treating TBI patients in LMIC health systems and such utility can be expanded through determining different threshold probabilities for various interventions. | |
| dc.identifier.uri | ||
| dc.subject | Neurosciences | |
| dc.subject | Epidemiology | |
| dc.subject | Public health | |
| dc.subject | Decision curve analysis | |
| dc.subject | LMICs | |
| dc.subject | Machine learning | |
| dc.subject | Neurosurgery | |
| dc.subject | Prognostic model | |
| dc.subject | Traumatic brain injury | |
| dc.title | Developing a Machine Learning Based Clinical Decision-Making Tool for Traumatic Brain Injury Patients in Moshi, Tanzania | |
| dc.type | Master's thesis | |
| duke.embargo.months | 24 | |
| duke.embargo.release | 2025-05-25T00:00:00Z |