Machine Learning For Predicting CAP Severity

This systematic review evaluated the efficacy of machine learning (ML) models in predicting community-acquired pneumonia (CAP) severity, including mortality and ICU admission. Analysis of 11 studies involving 351,365 CAP patients showed ML models achieved AUROC scores ranging from 0.57 to 0.98, with mixed performance compared to traditional tools. However, variations in variable selection and lack of reproducible data limit their validity. Future research should prioritize validating ML models across multiple cohorts to ensure robust performance and demonstrate patient outcome and resource utilization benefits.