D ial M ed: A Dataset for Dialogue-based Medication Recommendation (2022)

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 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 .

## introduction the outbreak of covid-19 has challenged the healthcare systems and led to millions of patients facing delays in diagnosis and treatment. as an essential complement to the traditional face-toface medicine, telemedicine relieved the therapeutic stress caused by the diversion of medical resources. according to the report of wedoctor 1 , an online health consultation platform in china, about 1.2 million patients conducted online medical consultations during the covid-19 pandemic. telemedicine can increase the availability of medical treatment, reduce healthcare costs, and improve the quality of care. consequently, it has attracted increasing attention due to its vast application potential. our study found that around 31% of online consultations are about what medications the patients should take based on their current conditions foot_0 . figure 1 demonstrates a typical medication consultation dialogue. the patient reported the health issues initially, with some personal information, such as gender and age. then the doctor asked for further information (e.g., symptoms and disease history) about the patient. finally, the doctor provided medication advice based on the gathered information and clinical experience. existing studies on medication recommendation are primarily based on ehrs @xcite @xcite , accumulatively collected according to a diagnostic procedure in clinics. however, the doctors will omit some details of interactions with patients in ehrs, which are essential for the automatic medication recommendation. compared to ehrs, medical dialogues retain original interactions between doctors and patients, containing more rich information. to this end, medical dialogue-based medication recommendation is a promising and challenging task. therefore, in this work, we study the new task, namely dialogue-based medication recommendation. due to the lack of available datasets, we firstly construct a high-quality online medical dialogues dataset (dialmed) for this task. it contains 11, 996 consultation dialogues, 16 diseases from 3 different departments, and 70 related common medications. then, to further advance the research of this task, we propose a dialogue structure and disease knowledge aware network (ddn). in ddn, for the input dialogue, we first utilize a pre-trained language model to extract the semantic information of each utterance. a mechanism named qa dialogue graph is designed to understand the ques-tions&answers implied in utterances, and then we apply graph attention network on this qa graph to get the dialogue embedding. meanwhile, for the input disease, we use its identity to query the entity in a knowledge graph cmekg 3 , and input the dialogue embedding to a graph attention network to get contextual disease embedding. the two embeddings are fused to make the medication prediction. moreover, we conduct extensive experiments to show that the proposed method can effectively recommend medications with medical dialogues. our contributions can be summarized as follows: medication recommendation. existing medication recommendations are mainly based on 3 http://cmekg.pcl.ac.cn/ ehrs. it could be categorized into instancebased and longitudinal-based recommendation methods @xcite . instance-based methods are based on the current health conditions extracted from recent visit @xcite . for example, @xcite proposed a multi-instance multi-label learning framework to predict medication combination based on patient's current diagnoses. longitudinalbased methods leverage the temporal dependencies among clinical events @xcite @xcite @xcite @xcite . among them, @xcite combined the power of graph neural networks and bert for medication recommendation. @xcite proposed a drug-drug interactions (ddi)-controllable drug recommendation model to leverage drugs' molecule structures and model ddis explicitly. unlike the work mentioned above, dialoguebased medication recommendation task is more challenging in practice due to the noisy and sparse data. because of the privacy issue, it is difficult to get historical dialogues of a patient on online consultation platforms. so we perform the medication recommendation solely based on the current medical dialogues. ## corpus description in this section, we introduce the construction details and statistics of dialmed, and its comparison with other studies. ## conclusions in this paper, we studied a new task, namely dialogue-based medication recommendation. first, we presented the first high-quality medical dialogue dataset dialmed for this task. and then we implemented several baselines, as well as designed a dialogue structure and external disease knowledge aware model. experimental results show that medication recommendation quality can be enhanced with the help of dialogue structure and external disease knowledge.