Knowledge Graph Enhanced Clinical Decision Support System for Personalized Healthcare Analytics
Keywords:
clinical decision support, knowledge graph, personalized healthcare, semantic interoperability, explainability, fairness, system architectureAbstract
The escalating complexity of modern healthcare, characterized by vast heterogeneous data streams and an expanding biomedical knowledge base, necessitates decision support systems that move beyond rule-based alerts toward context-aware, personalized recommendations. This paper presents a conceptual and architectural framework for a clinical decision support system (CDSS) that integrates knowledge graphs with machine learning analytics to deliver personalized healthcare insights. Unlike conventional CDSS that rely on static ontologies or isolated predictive models, the proposed system leverages a continuously evolving knowledge graph that encodes multimodal clinical data, biomedical literature, patient histories, and treatment outcomes. The architecture emphasizes modularity, semantic interoperability, and real-time update capabilities, enabling the system to adapt to new evidence and individual patient trajectories. We examine structural trade-offs between expressiveness and scalability, the governance challenges of maintaining data provenance and consent, and the infrastructure considerations for deployment across diverse healthcare settings. Robustness and fairness are addressed through explicit mechanisms for bias auditing, handling missing data, and ensuring transparency in recommendation generation. Policy implications, including regulatory compliance, liability, and equitable access, are discussed in the context of existing frameworks such as the General Data Protection Regulation and the Health Insurance Portability and Accountability Act. The paper contributes a systems-level perspective on the design and deployment of knowledge-graph-enhanced CDSS, highlighting critical dimensions that must be balanced for sustainable and trustworthy clinical adoption.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
