Development and External Validation of a 72-Hour Dynamic Trajectory-Based Machine Learning Model for Mortality Prediction in ICU Patients with Sepsis and Pre-Existing Hypertension
This study developed and externally validated a 72-hour landmark XGBoost model integrating static and dynamic clinical features that outperformed conventional severity scores in predicting in-hospital mortality for ICU patients with sepsis and pre-existing hypertension.