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

TL;DR

medicX-KG is a pharmacist-focused knowledge graph that integrates multiple drug data sources to support clinical and regulatory decision-making, improving access to comprehensive and accurate medicinal information.

Contribution

This paper introduces medicX-KG, a novel semantic knowledge graph tailored for pharmacists that consolidates diverse drug data sources and supports decision-making within a healthcare platform.

Findings

Supports queries on drug interactions and adverse reactions

Effectively integrates data from BNF, DrugBank, and MMA

Reduces reliance on fragmented drug information sources

Abstract

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.