Neuro-symbolic artificial intelligence for real-time sepsis mortality prediction

Sepsis mortality prediction in intensive care units represents a critical engineering challenge in healthcare systems, where timely intervention through intelligent decision support systems can significantly reduce mortality rates. Current clinical decision support systems rely on black-box machine learning models that lack the interpretability required for safe deployment in life-critical engineering applications. We develop a neuro-symbolic artificial intelligence framework that integrates Logic Tensor Networks with automated clinical knowledge extraction for interpretable mortality prediction. Our novel contributions include: (1) an automated pipeline for transforming clinical guidelines into first-order logic constraints using medical knowledge graphs and rule mining; (2) a dual axiom architecture with weak anchoring that enables context-adaptive learning while maintaining semantic grounding; and (3) a three-tier explainability framework providing concept-level, rule-level, and patient-level interpretability. Using 19,328 sepsis patients from a publicly available real-world clinical dataset, our system achieves 86.45% accuracy at 6-h prediction windows, outperforming traditional machine learning baselines while providing full transparency in decision-making. External validation on 6735 patients demonstrates preserved ranking ability despite domain shift challenges. • Neuro-symbolic sepsis mortality prediction with 86.45% accuracy and full transparency. • Automated clinical guideline integration via medical knowledge graph and rule mining. • Three-tier explainability framework for concept, rule, and patient-level reasoning. • Outperforms machine learning baselines on sepsis patients. • Addresses artificial intelligence interpretability gap in critical care.