DialMed: A Dataset for Dialogue-based Medication Recommendation
- URL: http://arxiv.org/abs/2203.07094v1
- Date: Tue, 22 Feb 2022 05:12:29 GMT
- Title: DialMed: A Dataset for Dialogue-based Medication Recommendation
- Authors: Zhenfeng He and Yuqiang Han and Zhenqiu Ouyang and Wei Gao and Hongxu
Chen and Guandong Xu and Jian Wu
- Abstract summary: We make the first attempt to recommend medications with the conversations between doctors and patients.
We construct DialMed, the first high-quality dataset for medical dialogue-based medication recommendation task.
- Score: 20.08110449216702
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 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 graph attention network is utilized 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/Hhhhhhhzf/DialMed.
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