Using a Machine Learning Approach to Predict Snakebite Envenoming Outcomes Among Patients Attending the Snakebite Treatment and Research Hospital Kaltungo, Northeastern Nigeria

The Snakebite Treatment and Research Hospital (SBTRH) is a leading center for snakebite envenoming care and research in sub-Saharan Africa, treating over 2,500 snakebite patients annually. Despite routine data collection, routine analyses are seldom conducted to identify trends or guide clinical practices. This study retrospectively analyzed 1,022 snakebite cases at SBTRH from January to June 2024. Most patients were adults (62%) and were predominantly male (72%). Key factors such as age, sex, and time between bite and hospital presentation were associated with outcomes, including recovery, amputation, debridement, and death. Adult males who took more than four hours to arrive to hospital were identified as a high-risk group for poor outcomes. Using patient characteristics, an XGBoost model was developed, which achieved an area under the received-operator curve (AUROC) of 0.484 with all patient characteristics, and 0.529 when using a simplified subset of characteristics. Model performance was compared to random-forest and logistic regression models. In general, for all models, performance tended to increase slightly when using a simplified set of features, which may be of significance to resource-limited settings like SBTRH, however, more research is needed to build more robust models. These findings underscore the need for targeted interventions for high-risk groups, optimization of antivenom administration strategies and integration of machine learning-driven decision support tools in low-resource-limited clinical settings.