Machine learning models for early mortality prediction in severe fever with thrombocytopenia syndrome.

Severe fever with thrombocytopenia syndrome (SFTS) is a high-fatality viral disease where early mortality risk prediction is vital for clinical management. This retrospective multicenter cohort study enrolled 1,690 hospitalized SFTS patients from five Chinese hospitals (2014-2023) to develop, validate, and deploy an interpretable machine learning (ML) model for early mortality risk assessment. Using LASSO regression for feature selection then comparing eight ML algorithms, the XGBoost model achieved an AUC of 0.916 in the training cohort and 0.905 in the temporal validation cohort. SHapley Additive exPlanations (SHAP) analysis identified six key predictors, which were used to deploy a real-time, open-access web-based tool (https://sftsprognosis.com) that provides individualized risk predictions with visual explanations. The XGBoost model has the potential to enhance timely clinical decision-making, facilitate efficient allocation of critical care resources, and provide a generalizable framework for applying machine learning in the management of emerging infectious diseases.