Graph-Driven Decision Support from Bedside to Forecast
DOI:
https://doi.org/10.5281/g0ayp746Keywords:
Knowledge Graphs, Clinical Decision Support, Predictive Analysis, Event-driven, Interoperability Standards,Intelligent Patient Knowledge Graphs,Clinical Decision Support Systems,Real-Time Healthcare Analytics,Predictive Care Intelligence,Electronic Health Records (EHR) Integration,Medical Knowledge Representation,AI-Driven Clinical Decision Making,Healthcare Knowledge Graphs,Semantic Interoperability in Healthcare,Predictive Healthcare Analytics.Abstract
Real-time clinical decision-making depends on timely, relevant insight into dynamic patient conditions and evolving care trajectories. Intelligent knowledge graphs that integrate each patient's temporal, multisource data with graphical causal knowledge can generate event-driven alerts for clinicians as conditions change. Predictive care, meanwhile, benefits from temporal modeling of this data to support risk stratification and identify predictive features — for example, tracking a patient's trajectory to detect meaningful shifts and assess the risk associated with those changes. Building effective patient knowledge graphs for modern healthcare raises key research questions around interoperability, data quality, and patient safety.
Knowledge graphs offer powerful representation and reasoning capabilities to support decision-making across domains. While knowledge representation at the graph level is well established, enabling real-time, graph-based reasoning and decision-making — responsive to continuously changing data — requires an event-driven perspective. In healthcare, these two dimensions are tightly intertwined: data from Electronic Health Records (EHR), imaging, labs, genomics, and wearables feeds the patient knowledge graph that underpins real-time clinical decision coordination. Together, events, triggers, predictive models, and causal relationships form a semantically rich foundation for representing and reasoning about patient care.
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