The development of reliable mortality risk stratification models is an active research area in computational healthcare. Mortality risk stratification provides a standard to assist physicians in evaluating a patient's condition or prognosis objectively. Particular interest lies in methods that are transparent to clinical interpretation and that retain predictive power once validated across diverse datasets they were not trained on. We've developed a hybrid model integrating mechanistic, clinical knowledge with mathematical and machine learning models to predict ICU mortality using ICD codes. A tree-structured network connecting independent modules that carry clinical meaning is implemented for interpretability. The trained model is then validated on external datasets from different hospitals, demonstrating successful generalization capabilities.