Machine learning-based early warning system for hemodynamic deterioration in cardiovascular ICU patients: a bidirectional cross-validation study

We successfully developed and validated a machine learning-based early warning system for hemodynamic deterioration in cardiovascular ICU patients. The bidirectional cross-validation approach provides robust evidence for model generalizability, while the multi-level risk stratification system and SHAP-based interpretability offer practical clinical decision support. This system demonstrates significant potential to enhance early identification rates, improve patient outcomes, and optimize healthcare resource utilization efficiency.