M id M ed: Towards Mixed-Type Dialogues for Medical Consultation (2023)

Abstract: Most medical dialogue systems assume that patients have clear goals (seeking a diagnosis, medicine querying, etc.) before medical consultation. However, in many real situations, due to the lack of medical knowledge, it is usually difficult for patients to determine clear goals with all necessary slots. In this paper, we identify this challenge as how to construct medical consultation dialogue systems to help patients clarify their goals. For further study, we create a novel human-to-human mixed-type medical consultation dialogue corpus, termed MidMed, covering four dialogue types: task-oriented dialogue for diagnosis, recommendation, QA, and chitchat. MidMed covers four departments (otorhinolaryngology, ophthalmology, skin, and digestive system), with 8,309 dialogues. Furthermore, we build benchmarking baselines on MidMed and propose an instruction-guiding medical dialogue generation framework, termed InsMed, to handle mixed-type dialogues. Experimental results show the effectiveness of InsMed.

## introduction current medical dialogue systems @xcite @xcite ) mainly focus on diagnosis by obtaining symptoms and then making diagnosis automatically. these dialogue systems have shown significant potential and alluring technological value to simplify diagnostic procedures @xcite . previous works assume that patients have explicit goals (medicine querying, surgical operation querying, etc.), and perform in the way of taskoriented dialogue to accomplish patients' goals. however, explicit patient goals are usually unavailable in real-world scenarios. for example, a patient wants to consult about his itchy skin but lacks medical knowledge. thus, it is difficult for the patient to decide which slots (e.g. medicine or a surgical operation) are needed. to figure out explicit patient goals, medical consultation services are needed, which provide advice of treatment, medicine, food, etc., as shown in figure 1 . however, those medical consultation services are under explored in previous works. to facilitate the study of medical consultation, we construct a new human-to-human mixed-type dialogue dataset for medical consultation (midmed), covering five dialogue types: task-oriented dialogue for diagnosis, knowledgegrounded dialogue, qa, recommendation, and chitchat. midmed is constructed by revising dialogues of meddialog (a human-to-human medical diagnosis dialogue dataset) @xcite . as shown in figure 1 , a patient queries about "sweaty hands", and has no explicit goal for medicine or a surgical operation. in the scenario, the doctor first collects the symptoms and makes a diagnosis. to help clarify the patient's goal, the doctor further recommends medicine and food, replies for foods to avoid, and gives emotional comfort. through the consultation, the patient determines to apply "dexamethasone cream" and have more "tomatoes". finally, midmed is obtained, containing 8,175 dialogues and 98,000 utterances, with at least three dialogue types in each dialogue. to promote research on medical consultation dialogue systems, we conduct benchmarking experiments on midmed for end-to-end dialogue generation. furthermore, to generate informative and relevant responses with dialogue topic sequences, inspired by @xcite ; @xcite , we present an instruction-guiding medical dialogue generation framework (insmed) to handle mixed-type dialogues. insmed is composed reference knowledge target topic dialogue diagnosis patient: 手脚多汗和干燥蜕皮。夏天手上不戒汗，蜕皮现象严重 。 (i have excessive sweating and dry peeling in hands and feet. in summer, skin peeling is serious and hands are sweaty.) doctor: 有没有瘙痒的感觉? (do you feel itching?) patient: 没有痒瘙的感觉。(i do not feel itching.) doctor: 初步应该考虑汗疱疹，是湿疹的一种表现。(it considered to be sweat, which is a manifestation of eczema.) patient: 多谢医生。(thanks.) recommend medicine doctor: 推荐进行药物治疗。用温水泡手半小时，然后涂地塞米松乳膏。 (drug therapy is recommended. soak your hands in warm water for half an hour, then apply dexamethasone cream.) patient: 好的医生，我会照做的。(i will do as you tell me.) recommend diet doctor: 除了药物治疗，饮食方面也需要注意。推荐吃猕猴桃，对治疗湿疹具有一定的作用。 (besides medication, diet is also important. i recommend you to have kiwi fruits, which benefits treating eczema.) patient: 不太喜欢吃猕猴桃。(i do not like kiwis fruits.) doctor: 那可以吃西红柿，富含有大量的维生素c，对治疗湿疹也有帮助。 (alternatively, you can have tomatoes, which are rich in vitamin c. it is also helpful for treating eczema.) consultant food to avoid patient: 好的医生。那有什么不能吃的吗？(thanks. is there anything i should not eat?) doctor: 湿疹不建议吃辛辣刺激的食物、油腻的食物，不建议饮酒。 (you should avoid spicy food, greasy food, and alcohol.) emotional comfort patient: 感谢医生。您的建议太宝贵了！(thanks. your advice is valuable.) doctor: 客气了。你的病情不严重，平常多注意很快就能好了。 (you are welcome. your condition is not serious. best wishes.) task-oriented dialogue for diagnosis recommendation qa chitchat eczema bonmopirocin ointment dexamethasone cream kiwi fruit tomato spicy food greasy food alcohol medicine medicine diet diet food to avoid food to avoid food to avoid of a dialogue topic selection, a reference knowledge selection, and an instruction-based response generation module. specifically, the topic selection module and the reference knowledge selection module are designed to pick suitable dialogue topics and reference knowledge for generating responses, respectively. then, dialogue topics and reference knowledge are converted to instructions in natural language with well-designed templates. for example, an instruction is "in the next utterance, the doctor will recommend a diet. the recommended diet is fruits and vegetables". these instructions are concatenated with dialogue context as the input to generation models. this work makes the following contributions: ## dataset collection in this section, we describe the three steps for midmed construction: (1) selecting basic diagnosis dialogue data; (2) constructing annotation guidance; ## method during training, a dialogue, with a sequence of utterances between a patient and a doctor, is given. then, the dialogue is processed into a set of samples {(s i , t i )} ∈ d, where t i is i-th target doctor response, s i is the concatenation of all former utterances before t i , and d is the training dataset. dialogue generation is formulated as a sequenceto-sequence generation problem, which aims to generate t i conditioned on s i . insmed has three modules, dialogue topic selecting, reference knowledge selection, and the instruction-guided generation module. the dialogue topic prediction and the reference knowledge selection module aim to obtain dialogue topics and reference knowledge, respectively. then, for better generation performance, these two types of information are transformed into instructions in natural language. finally, instructions are concatenated with context, as the input to generation models. next, the above modules are introduced. ## experiments and results this section introduces experimental setting, data and evaluation metrics, baselines, automatic evaluations, human evaluations, and the ablation study. ## conclusion this work identified the challenge of helping patients clarify their goals through medical consultations. to address this challenge, this work proposed a novel task, medical consultation over mixed-type dialogue, and collected a new chinese human-tohuman mixed-type dialogue dataset, in which each session has rich variability of dialogue types with natural topic transitions. to facilitate further research, we conducted benchmarking experiments on midmed for end-to-end dialogue generation and proposed an instruction-guiding medical dialogue generation framework insmed. experimental results show the effectiveness of insmed. in the future, we will investigate the possibility of crossdepartments (e.g. dermatology and endocrinology) medical consultation at low cost.