Human-in-the-Loop Knowledge Graph Mining for Personalized Healthcare Decision Support
Keywords:
knowledge graphs, human-in-the-loop, healthcare decision support, personalized medicine, system architecture, fairness, governanceAbstract
The increasing digitization of clinical information has led to the proliferation of large-scale, heterogeneous biomedical data repositories that are exceptionally challenging to translate into actionable decision support. Knowledge graphs offer a structured yet flexible representation for integrating disparate data sources, capturing complex semantic relationships among diseases, treatments, genomic markers, and patient outcomes. However, fully automated graph mining pipelines often fail to meet the nuanced interpretability, safety, and contextual personalization requirements of real-world clinical environments. This paper presents a systems-level examination of human-in-the-loop knowledge graph mining architectures for personalized healthcare decision support. It explores the structural trade-offs between automation and expert engagement, proposing that hybrid designs which interleave embedding-based pattern discovery with clinician-guided rule refinement yield more robust and trustworthy outputs. The discussion examines multi-layered architecture models comprising data ingestion, graph construction, embedding and relational learning, interactive querying, and explanation layers. Substantial attention is given to governance challenges including bias amplification across interconnected clinical entities, fairness in recommendation distributions, continuous monitoring for model drift, and regulatory compliance under evolving legal frameworks. The paper further addresses deployment considerations such as infrastructure scalability, latency constraints in acute care settings, cross-institutional data federation, and long-term system sustainability. Without resorting to algorithmic formalisms, it provides a conceptual analysis of how human feedback loops can reshape graph representation learning, improve diversification in top-k clinical rule discovery, and enforce medical plausibility constraints. By synthesizing perspectives from systems engineering, artificial intelligence, and health informatics, the paper articulates a forward-looking vision in which personalized decision support systems are not static artifacts but evolving socio-technical infrastructures that balance computational power with domain expertise, ethical oversight, and institutional accountability.
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