Connected Evidence Scalable Graph Risk Prediction
Keywords:
Real-time clinical decision support, scalable graph intelligence, entity and relationship modeling, temporal graphs, risk stratification, causal inference, event sequencing, ensemble learning.Abstract
Scalable graph intelligence offers a way to integrate clinical knowledge in real time to support immediate clinical decision-making — particularly knowledge expressed as feature-extraction rules for clinical risk stratification tasks such as early detection of acute disease. By modeling entities as graphs, structured data streaming from multiple hospitals can be represented as a temporal graph, capturing either the temporal or causal structure of the data depending on whether it is used for rapid, real-time risk stratification or stored for later analysis. Both representations serve a dual purpose: meeting the immediate need to extract features for risk stratification, and subsequently supporting unified, real-time integration of clinical knowledge.
Existing research on applying graphs to clinical data typically adopts one of two narrow perspectives — knowledge graphs for integrating clinical rules, or temporal graphs for risk stratification — treating them as largely separate problems. In practice, however, the two are closely linked: clinical knowledge is readily translated into features, and risk-stratification rules inevitably carry an implicit temporal structure. As a result, clinical knowledge is often captured concisely in rule form, and rule-based risk stratification approaches remain widely used. Yet these rules remain constrained by the limitations of manual knowledge engineering, underscoring the need for pathways that enable real-time, automated knowledge integration. Moreover, the feature sets produced by current approaches often prove insufficient for the early detection of acute, life-threatening conditions.
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