Explainable early sepsis prediction using hybrid TCN-ransformer and retrieval-augmented clinical reasoning

Sepsis is a life-threatening infection associated with a dysregulated host response, which is one of the major causes of death in intensive care units around the world. Early detection of the onset of sepsis is essential to enhancing patient outcomes, however the rule-based scoring systems can only be described as reactive, and do not take advantage of the rich temporal information contained within continuous monitoring data in the ICU. An explainable clinical decision support system (Early sepsis prediction) is introduced in this paper, which combines a hybrid Temporal Convolutional Network and Transformer architecture with SHAP-based explainability and retrieval-augmented clinical report generation. The proposed model works on 12-h sliding windows of multivariate ICU times series derived using the MIMIC-IV database. The proposed model which is a hybrid TCN-Transformer architecture has an AUROC of 0.777 and PR-AUC of 0.766 and it performs better than baseline models such as Random Forest, XGBoost, LSTM, and a plain TCN. SHAP gradient attribution is used to provide post-hoc explainability which provides both patient-level feature importance across both feature and temporal dimensions along with a clinical report based on verified clinical guidelines. The system is not meant to be a replacement of clinical judgment, but merely a decision-support tool to supplement it.