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.
@article{arxiv.2510.27274,
title = {Traceable Drug Recommendation over Medical Knowledge Graphs},
author = {Yu Lin and Zhen Jia and Philipp Christmann and Xu Zhang and Shengdong Du and Tianrui Li},
journal= {arXiv preprint arXiv:2510.27274},
year = {2025}
}