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medicX-KG: A Knowledge Graph for Pharmacists' Drug Information Needs

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arxiv 2506.17959 v1 pith:BMUEDQTV submitted 2025-06-22 cs.AI

Lizzy Farrugia , Lilian M. Azzopardi , Jeremy Debattista , Charlie Abela This is my paper · ORCID
classification cs.AI
keywords medicx-kgpharmacistsdatadrugincludingknowledgesemanticdesign
verification ladder T0 review T1 audit T2 compute T3 formal
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The role of pharmacists is evolving from medicine dispensing to delivering comprehensive pharmaceutical services within multidisciplinary healthcare teams. Central to this shift is access to accurate, up-to-date medicinal product information supported by robust data integration. Leveraging artificial intelligence and semantic technologies, Knowledge Graphs (KGs) uncover hidden relationships and enable data-driven decision-making. This paper presents medicX-KG, a pharmacist-oriented knowledge graph supporting clinical and regulatory decisions. It forms the semantic layer of the broader medicX platform, powering predictive and explainable pharmacy services. medicX-KG integrates data from three sources, including, the British National Formulary (BNF), DrugBank, and the Malta Medicines Authority (MMA) that addresses Malta's regulatory landscape and combines European Medicines Agency alignment with partial UK supply dependence. The KG tackles the absence of a unified national drug repository, reducing pharmacists' reliance on fragmented sources. Its design was informed by interviews with practicing pharmacists to ensure real-world applicability. We detail the KG's construction, including data extraction, ontology design, and semantic mapping. Evaluation demonstrates that medicX-KG effectively supports queries about drug availability, interactions, adverse reactions, and therapeutic classes. Limitations, including missing detailed dosage encoding and real-time updates, are discussed alongside directions for future enhancements.

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8 extracted references · 1 canonical work pages

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