R eal M ed D ial: A Real Telemedical Dialogue Dataset Collected from Online C hinese Short-Video Clips (2022)

Abstract: Intelligent medical services have attracted great research interests for providing automated medical consultation. However, the lack of corpora becomes a main obstacle to related research, particularly data from real scenarios. In this paper, we construct RealMedDial, a Chinese medical dialogue dataset based on real medical consultation. RealMedDial contains 2,637 medical dialogues and 24,255 utterances obtained from Chinese short-video clips of real medical consultations. We collected and annotated a wide range of meta-data with respect to medical dialogue including doctor profiles, hospital departments, diseases and symptoms for fine-grained analysis on language usage pattern and clinical diagnosis. We evaluate the performance of medical response generation, department routing and doctor recommendation on RealMedDial. Results show that RealMedDial are applicable to a wide range of NLP tasks with respect to medical dialogue.

## introduction the covid-19 pandemic has dramatically changed how outpatient care is delivered in healthcare practices. to decrease the risk of transmitting the virus to either patients or healthcare workers within their health practice, providers are deferring or selectively prohibiting in-person visits, but fortunately, they are usually converting in-person visits to telemedicine visits @xcite . during the telemedicine, patients describe their symptoms of suffered diseases and/or adverse reactions of the taking drugs to doctors, while doctors provide medical consultations through online video conferences. although telemedicine is convenient and timely for disease diagnoses, the continuous growth of telemedicine visits significantly increases the burden and workload of doctors, and meanwhile, the health conditions of remote patients become * corresponding author increasingly difficult to be tracked. thus, how to relieve the burden of doctors and effectively track the patient's health conditions remains an open research question. researchers from related fields are trying to solve this issue by developing medical dialogue systems to serve as virtual doctors, which greatly facilitates users to obtain medical and healthcare information. recent advances in medical dialogue systems have benefited medical applications such as psychological consultation @xcite , elderly care @xcite , and disease pre-diagnosis @xcite . to build effective medical dialogue systems, related studies are focusing on optimizing medical dialogue from various aspects, including automatic diagnosis @xcite , medical information extraction @xcite , medical slot filling @xcite , and medical conversational summarization @xcite . although these researches have improved the performance of medical dialogue, this challenging task is still facing great difficulty in generating effective responses due to the particularity and professionalism of the medical field. in general, several key challenges have not been thoroughly considered in the current medical dialogue datasets. first, as shown in third, doctors, especially domain experts, are usually very busy and do not have enough time to answer online questions frequently. in order to attract more patients to use the health community, the companies have to hire graduate students studying in medical schools as online doctors. they will be paid when replying patients' questions. compared with experts' replies, the quality of the answers from graduate students is usually not very high in some dialogues. the low quality issue of existing datasets also impedes the development and learning of medical dialogue models. to tackle all the aforementioned limitations, in this paper, we construct a real-time, high-quality, and large-scale medical dialogue dataset named realmeddial, which is extracted from online chinese short-video clips. in particular, the videos are downloaded from a popular chinese video-based social media named kuaishou foot_0 , where many medical physicians record the short-videos when they communicate with their online or offline patients and post them to kuaishou. those short-videos are all real-time medical consultations, which are high quality and representative for diagnosing diseases. moreover, the contents between doctors and patients not only include disease diagnoses but also treatment plans as well as prognoses. we transcribe the real-scenario medical conversations into text, which is used to simulate real doctor-patient consultations for training effective medical dialogue models. besides, we also extract video titles, doctor profiles, disease, symptoms, and hospital departments. an example is shown in figure 1 . compared with existing medical dialogue datasets based on online health community, our dataset also has two extra advantages. the first advantage is that it enables us to conduct the modeling of language usage patterns. on online health community, doctor-patient conversations are often completed offline. offline language usage tends to adopt written expressions, which is quite different from the oral expressions in real medical consultation. since our dataset is realistic starting from the dialogue scenario, dialogue models based on our dataset are more conducive to better modeling the language usage patterns for training a robust response generation model. the second advantage is comprehensiveness of information. the constructed dataset not only contains medical conversations between doctors and patients during clinical consultations. we also collected and annotated a wide range of meta-data with respect to medical conversations including doctor profiles, hospital departments, diseases and symptoms. the meta-data can be used for finegrained modeling and analysis on medical dialogue. for example, doctor profiles could be incorporated into an expertise-specific dialogue model for personalized response generation. diseases and symptoms can be used to mine patients' dialogue intents for precise clinical treatments. the main contributions of this work are summarized as follows. the rest of this paper is organized as follows. section 2 reviews related work to our paper. section 3 introduces our data collection including data source, cleaning, annotation strategy and statistics. section 4 provides our experiments of dialogue generation, department routing and doctor recommendation on the constructed dataset. section 5 concludes this work and provides future directions for the constructed dataset. ## experiments the realmeddial dataset is built from realscenario medical consultations, and thus, it can be used to simulate medical dialogue in a real environment for developing effective automatic medical chatbot. except for generating useful responses of medical dialogue, realmeddial can also be used for the department routing task and the doctor recommendation task. next, we provide detailed evaluation on these three tasks, respectively. ## conclusion and future work to facilitate automatical medical consultation, we construct realmeddial, a high-quality chinese dataset of medical dialogue based on real scenario medical consultation from online short-video clips. real medical consultation contributes to learning more powerful and human-like dialogue models by considering communications in reality between doctors and patients instead of question answeringbased communications on online health community. we collected and annotated a wide range of meta-data with respect to the medical dialogue including titles of short-video clips, doctor profiles, hospital departments, diseases and symptoms for fine-grained analysis on language usage pattern and clinical diagnosis. we evaluated the performance of medical response generation, department routing and doctor recommendation on realmed-dial. results show that realmeddial is applicable to various medical dialogue tasks. as for future work, we will build personalized dialogue models by incorporating more professional knowledge into medical response generation.