Temporal Sepsis Modeling: a Relational and Explainable-by-Design Framework

Sepsis remains one of the most complex and heterogeneous syndromes in intensive care. While deep learning models achieve competitive performance in early sepsis prediction, their decision processes often remain difficult to interpret clinically, and explainability is typically added only through post-hoc methods. We propose an explainable-by-design framework based on a relational approach: temporal EHR data are represented in a relational schema, flattened via MDL-based propositionalisation into compact human-readable features, and classified using a selective Fractional Naive Bayes classifier. Evaluated on MIMIC-III (3,940 patients, 10-fold cross-validation), our approach achieves AUC = 0.983 - competitive with XGBoost (0.985) and CatBoost (0.985), and superior to LSTM (0.945) - with only 98 selected variables and a 1 MB model footprint. Unlike post-hoc methods, interpretability is native and fourfold: univariate, global, local, and counterfactual.