This study developed and successfully validated a machine learning (ML) model designed to provide an early prediction of septic shock in intensive care unit (ICU) patients diagnosed with sepsis.

Best Performer: Out of six algorithms tested, the Random Forest (RF) model demonstrated the best overall performance, achieving an Area Under the Curve (AUC) of 0.785 and a balanced accuracy of 0.717.

Ready for Clinical Use: Since the model is built using routinely collected ICU data, it can be easily integrated into Electronic Health Record (EHR) systems for automated, real-time risk assessment.

Key Predictors Identified: Using SHAP (Shapley Additive explanations) analysis for model interpretability, the study identified the most influential factors in predicting septic shock: SOFA score, Heart rate, Creatinine, SAPS II, OASIS.

These findings offer valuable tools for the early detection and management of septic shock, potentially leading to improved clinical decision-making and better patient outcomes in critical care settings.

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