Development and validation of a clinical prediction model for in-hospital mortality of severe pneumonia based on machine learning

Older age, increased BUN level, complication of septic shock, tracheotomy application, retention catheterization application, oral Chinese herbal decoction, and TCM syndrome (pathogenic qi falling into and prostration syndrome) may be potential risk factors that affect mortality in severe pneumonia, while application of tracheotomy and oral Chinese herbal decoction were associated with reduced mortality. The XGBoost model exhibits superior overall performance in predicting hospital mortality risk for severe pneumonia, greater than traditional scoring systems such as Pneumonia Severity Index (PSI), Sequential Organ Failure Assessment (SOFA), and Acute Physiology and Chronic Health Evaluation II (APACHE II), which assists clinicians in prognostic assessment, resulting in improved therapeutic strategies and optimal resource allocation for patients.