A Semantic Approach to Clinical Risk Detection: Early Warning Systems with Knowledge Graphs

Detecting early signs of clinical deterioration is vital for ensuring patient safety and enabling timely intervention. This paper introduces a hybrid Early Warning System (EWS) that integrates physiological monitoring with semantic symp-tom reasoning to support clinical decision-making. The system leverages automated machine learning to identify nonlinear patterns in historical vital sign data, while a curated knowledge base links patient-reported symptoms to standardized clinical concepts. A conversational large language model guides user interaction, asking context-aware questions to clarify ambiguous input and improve data completeness. Once both physiological and symptom-based inputs are harmonized, the system generates an interpretable EWS score and suggests potential diagnoses or symptom clusters. This approach enhances predictive accuracy while maintaining transparency and promoting trust among clin-icians. By combining data-driven modeling with semantic reasoning and interactive AI, the proposed framework demonstrates a promising direction for human-centered, next-generation clinical decision support systems.