An interpretable machine learning algorithm enables dynamic 48-hour mortality prediction during an ICU stay

Accurate short-term mortality prediction is essential for optimizing ICU management and improving patient outcomes. Many existing models rely on static data and do not reflect the dynamic progression of critical illness. This study aimed to develop and validate an interpretable machine learning algorithm that enables dynamic 48-hour mortality prediction throughout the ICU stay.

We conducted a retrospective cohort study using electronic health records of 9,786 ICU patients treated between 2018 and 2022 at a German university hospital. A machine learning model was developed to predict 48-hour mortality, updated every 24 hours during the ICU stay. We trained and evaluated a Light Gradient-Boosting Machine using nested cross-validation and assessed performance via area under the receiver operating characteristic curve. External validation was performed on the MIMIC-IV database. Feature importance was analyzed using SHAP values.

Here, we show that the Light Gradient-Boosting Machine algorithm (LGBM-48h) achieves AUROCs of 0.909 (95% CI: 0.901-0.917) in the training and 0.886 (95% CI: 0.878-0.895) in the testing dataset. External validation using the MIMIC-IV database yields an AUROC of 0.859 (95% CI: 0.849-0.870). The model enables effective risk stratification across the ICU stay and reflects individual changes in patient status over time. Time-varying SHAP values improve interpretability by highlighting associated features.

LGBM-48h provides a dynamic and interpretable framework for short-term ICU mortality prediction. The model may support clinical decision-making and prioritization of care, but requires further validation in real-time and prospective settings.