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.

翻译：药物推荐系统旨在协助医疗专业人员根据患者的医疗状况选择合适的药物。当前最先进的方法利用深度学习技术改进药物推荐，但未能提供推荐推导过程的任何解释——在这种高风险应用中这是一个关键局限。我们提出了TraceDR，一种在医学知识图谱上运行的新型药物推荐系统，确保能够访问大规模高质量信息。TraceDR在多任务学习框架中同时预测药物推荐及相关证据，实现了药物推荐的可追溯性。为覆盖比现有研究更多样化的疾病和药物，我们设计了一个自动构建患者健康记录的框架，并发布了DrugRec——一个用于药物推荐的新型大规模测试平台。