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

• URL: http://arxiv.org/abs/2506.17959v1
• Date: Sun, 22 Jun 2025 09:28:48 GMT
• Title: medicX-KG: A Knowledge Graph for Pharmacists' Drug Information Needs
• Authors: Lizzy Farrugia, Lilian M. Azzopardi, Jeremy Debattista, Charlie Abela,
• Abstract summary: Leveraging artificial intelligence and semantic technologies, Knowledge Graphs (KGs) uncover hidden relationships and enable data-driven decision-making.<n>This paper presents medicX-KG, a pharmacist-oriented knowledge graph supporting clinical and regulatory decisions.<n>It forms the semantic layer of the broader medicX platform, powering predictive and explainable pharmacy services.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers [0.0]
  "Drug Insights," an open-source Retrieval-Augmented Generation (RAG), is designed to streamline medication lookup for healthcare workers in Africa.<n>By leveraging a corpus of Nigerian pharmaceutical data and advanced AI technologies, the system delivers accurate, context-specific responses with minimal hallucination.<n>Preliminary tests, including pharmacist feedback, affirm the tool's potential to improve drug information access.
  arXiv  Detail & Related papers  (2025-01-28T02:18:26Z)
• Natural Language-Assisted Multi-modal Medication Recommendation [97.07805345563348]
  We introduce the Natural Language-Assisted Multi-modal Medication Recommendation(NLA-MMR)<n>The NLA-MMR is a multi-modal alignment framework designed to learn knowledge from the patient view and medication view jointly.<n>In this vein, we employ pretrained language models(PLMs) to extract in-domain knowledge regarding patients and medications.
  arXiv  Detail & Related papers  (2025-01-13T09:51:50Z)
• MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding [48.348511646407026]
  We introduce the Medical dialogue with Knowledge enhancement and clinical Pathway encoding framework. The framework integrates an external knowledge enhancement module through a medical knowledge graph and an internal clinical pathway encoding via medical entities and physician actions.
  arXiv  Detail & Related papers  (2024-03-11T10:57:45Z)
• ABiMed: An intelligent and visual clinical decision support system for medication reviews and polypharmacy management [3.843569766201585]
  The aim of ABiMed is to design an innovative clinical decision support system for medication reviews and polypharmacy management. ABiMed associates several approaches: guidelines implementation, but the automatic extraction of patient data from the GP's electronic health record and its transfer to the pharmacist, and the visual presentation of contextualized drug knowledge using visual analytics.
  arXiv  Detail & Related papers  (2023-12-13T11:06:45Z)
• Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network [69.16939798838159]
  We propose EmerGNN, a graph neural network (GNN) that can effectively predict interactions for emerging drugs. EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths. Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.
  arXiv  Detail & Related papers  (2023-11-15T06:34:00Z)
• Navigating Healthcare Insights: A Birds Eye View of Explainability with Knowledge Graphs [0.0]
  Knowledge graphs (KGs) are gaining prominence in Healthcare AI, especially in drug discovery and pharmaceutical research. This overview summarizes recent literature on the impact of KGs in healthcare and their role in developing explainable AI models. We emphasize the importance of making KGs more interpretable through knowledge-infused learning in healthcare.
  arXiv  Detail & Related papers  (2023-09-28T16:57:03Z)
• A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises [59.4999994297993]
  This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs)<n>We thoroughly analyzed existing literature on HKGs, covering their construction methodologies, utilization techniques, and applications.<n>The review highlights the potential of HKGs to significantly impact biomedical research and clinical practice.
  arXiv  Detail & Related papers  (2023-06-07T21:51:56Z)
• Decision Support System for Chronic Diseases Based on Drug-Drug Interactions [67.9840225237587]
  This paper presents a Decision Support System, called DSSDDI, based on drug-drug interactions to support doctors prescribing decisions. The DDI module learns safer and more effective drug representations from the drug-drug interactions. The MD module considers the representations of patients and drugs as context, DDI and patients' similarity as treatment, and medication use as outcome.
  arXiv  Detail & Related papers  (2023-03-04T12:44:38Z)
• Knowledge-Driven New Drug Recommendation [88.35607943144261]
  We develop a drug-dependent multi-phenotype few-shot learner to bridge the gap between existing and new drugs. EDGE eliminates the false-negative supervision signal using an external drug-disease knowledge base. Results show that EDGE achieves 7.3% improvement on the ROC-AUC score over the best baseline.
  arXiv  Detail & Related papers  (2022-10-11T16:07:52Z)
• Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study [62.376800537374024]
  We study scientific LMs for KG completion, exploring whether we can tap into their latent knowledge to enhance biomedical link prediction. We integrate the LM-based models with KG embedding models, using a router method that learns to assign each input example to either type of model and provides a substantial boost in performance.
  arXiv  Detail & Related papers  (2021-06-17T17:55:33Z)
• A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective [4.746544835197422]
  We aim to help guide machine learning and knowledge graph practitioners who are interested in applying new techniques to the drug discovery field. We detail publicly available primary data sources containing information suitable for use in constructing various drug discovery focused knowledge graphs.
  arXiv  Detail & Related papers  (2021-02-19T17:49:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.