This study demonstrates that machine learning-based prediction models for sepsis can enhance clinical outcomes through accurate risk assessment during the critical first 24h of ICU admission.

Despite healthcare system variations between US and Chinese populations, our algorithm maintained reliable predictive performance, suggesting broad applicability.

These findings establish a foundation for larger multicenter clinical trials and the practical implementation of AI-assisted sepsis management in diverse healthcare settings.

This multicenter retrospective cohort study analyzed electronic medical records of sepsis patients using machine learning methods.

We evaluated model performance in predicting sepsis outcomes within the first 24 h of ICU admission across US and Chinese healthcare settings.

From 31 clinical features, machine learning models demonstrated significantly better predictive performance than traditional approaches for sepsis outcomes.

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