DIALMED: A dataset for dialogue-based medication recommendation

Zhenfeng HE, Yuqiang HAN, Zhenqiu OUYANG, Wei GAO, Hongxu CHEN, Guandong XU, Jian WU

Research output: Chapter in Book/Report/Conference proceedingChapters

1 Citation (Scopus)


Medication recommendation is a crucial task for intelligent healthcare systems. Previous studies mainly recommend medications with electronic health records (EHRs). However, some details of interactions between doctors and patients may be ignored or omitted in EHRs, which are essential for automatic medication recommendation. Therefore, we make the first attempt to recommend medications with the conversations between doctors and patients. In this work, we construct DIALMED, the first high-quality dataset for medical dialogue-based medication recommendation task. It contains 11, 996 medical dialogues related to 16 common diseases from 3 departments and 70 corresponding common medications. Furthermore, we propose a Dialogue structure and Disease knowledge aware Network (DDN), where a QA Dialogue Graph mechanism is designed to model the dialogue structure and the knowledge graph is used to introduce external disease knowledge. The extensive experimental results demonstrate that the proposed method is a promising solution to recommend medications with medical dialogues. The dataset and code are available at https://github.com/f-window/DialMed. Copyright © 2022 ACL.

Original languageEnglish
Title of host publicationProceedings of the 29th International Conference on Computational Linguistics, COLING 2022
Place of PublicationGyeongju
PublisherInternational Committee on Computational Linguistics
Publication statusPublished - 2022


He, Z., Han, Y., Ouyang, Z., Gao, W., Chen, H., Xu, G., & Wu, J. (2022). DIALMED: A dataset for dialogue-based medication recommendation. In Proceedings of the 29th International Conference on Computational Linguistics, COLING 2022 (pp. 721-733). International Committee on Computational Linguistics.


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