AI-derived CT biomarker score for robust COVID-19 mortality prediction across multiple waves and regions using machine learning.

ABSTRACT: This study aimed to develop a simple, interpretable model using routinely available data for predicting COVID-19 mortality at admission, addressing limitations of complex models, and to provide a statistically robust framework for controlled clinical use, managing model uncertainty for responsible healthcare application. Data from Belgium's first COVID-19 wave (UZ Brussel, n = 252) were used for model development. External validation utilized data from unvaccinated patients during the late second and early third waves (AZ Delta, n = 175). Various machine learning methods were trained and compared for diagnostic performance after data preprocessing and feature selection. The final model, the M3-score, incorporated three features: age, white blood cell (WBC) count, and AI-derived total lung

SUBMITTER: De Smet K 

PROVIDER: S-EPMC12328554 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature