Traceable Drug Recommendation over Medical Knowledge Graphs
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908622580088832 |
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| author | Lin, Yu Jia, Zhen Christmann, Philipp Zhang, Xu Du, Shengdong Li, Tianrui |
| author_facet | Lin, Yu Jia, Zhen Christmann, Philipp Zhang, Xu Du, Shengdong Li, Tianrui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_27274 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Traceable Drug Recommendation over Medical Knowledge Graphs Lin, Yu Jia, Zhen Christmann, Philipp Zhang, Xu Du, Shengdong Li, Tianrui Information Retrieval Machine Learning 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. |
| title | Traceable Drug Recommendation over Medical Knowledge Graphs |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2510.27274 |