MidMed: Towards Mixed-Type Dialogues for Medical Consultation
This addresses the problem of unclear patient goals in medical dialogue systems, though it is incremental as it builds on existing dialogue types and datasets.
The paper tackles the challenge of patients lacking clear goals in medical consultations by introducing a mixed-type dialogue corpus (MidMed) covering five dialogue types across four departments, and proposes an instruction-guiding framework (InsMed) that shows effectiveness in experiments.
Most medical dialogue systems assume that patients have clear goals (medicine querying, surgical operation querying, etc.) before medical consultation. However, in many real scenarios, 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. To mitigate this challenge, we propose a novel task and create a human-to-human mixed-type medical consultation dialogue corpus, termed MidMed, covering five dialogue types: task-oriented dialogue for diagnosis, recommendation, knowledge-grounded dialogue, QA, and chitchat. MidMed covers four departments (otorhinolaryngology, ophthalmology, skin, and digestive system), with 8,175 dialogues. Furthermore, we build baselines on MidMed and propose an instruction-guiding medical dialogue generation framework, termed InsMed, to address this task. Experimental results show the effectiveness of InsMed.