Traceable Drug Recommendation over Medical Knowledge Graphs

• URL: http://arxiv.org/abs/2510.27274v1
• Date: Fri, 31 Oct 2025 08:30:11 GMT
• Title: Traceable Drug Recommendation over Medical Knowledge Graphs
• Authors: Yu Lin, Zhen Jia, Philipp Christmann, Xu Zhang, Shengdong Du, Tianrui Li,
• Abstract summary: Drug recommendation (DR) systems aim to support healthcare professionals in selecting appropriate medications based on patients' medical conditions.<n>We propose TraceDR, a novel DR system operating over a medical knowledge graph (MKG)<n> TraceDR simultaneously predicts drug recommendations and related evidence within a multi-task learning framework.
• Score: 17.420983258275804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Drug recommendation (DR) systems aim to support healthcare professionals in selecting appropriate medications based on patients' medical conditions. State-of-the-art approaches utilize deep learning techniques for improving DR, but fall short in providing any insights on the derivation process of recommendations -- a critical limitation in such high-stake applications. We propose TraceDR, a novel DR system operating over a medical knowledge graph (MKG), which ensures access to large-scale and high-quality information. TraceDR simultaneously predicts drug recommendations and related evidence within a multi-task learning framework, enabling traceability of medication recommendations. For covering a more diverse set of diseases and drugs than existing works, we devise a framework for automatically constructing patient health records and release DrugRec, a new large-scale testbed for DR.

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