让药物推荐可追溯，基于知识图谱实现透明决策
Traceable Drug Recommendation over Medical Knowledge Graphs
- 在多任务学习框架中同时预测用药和证据链
- 构建了包含海量病历的DrugRec测试集，覆盖更广疾病与药物
- 适合医疗AI可解释性研究者及临床辅助系统开发者
药物推荐（DR）系统旨在根据患者病情辅助医生选择合适药物。现有先进方法虽采用深度学习提升性能，但无法揭示推荐背后的推理过程，这在高风险医疗场景中构成关键缺陷。本文提出TraceDR，一种基于医学知识图谱（MKG）的新型DR系统，可访问大规模高质量医疗信息。TraceDR在多任务学习框架下同时预测药物推荐结果与相关证据，实现用药决策的可追溯性。为覆盖比现有工作更广泛的疾病与药物，我们设计了一套自动构建患者健康记录的框架，并发布了DrugRec——一个全新的大规模DR测试基准。
原文摘要 · Abstract (English)
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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