MKA: A Scalable Medical Knowledge Assisted Mechanism for Generative
  Models on Medical Conversation Tasks
Ke Liang Sifan Wu Jiayi Gu
Using natural language processing (NLP) technologies to develop medical
chatbots makes the diagnosis of the patient more convenient and efficient,
which is a typical application in healthcare AI. Because of its importance,
lots of research have been come out. Recently, the neural generative models
have shown their impressive ability as the core of chatbot, while it cannot
scale well when directly applied to medical conversation due to the lack of
medical-specific knowledge. To address the limitation, a scalable Medical
Knowledge Assisted mechanism, MKA, is proposed in this paper. The mechanism
aims to assist general neural generative models to achieve better performance
on the medical conversation task. The medical-specific knowledge graph is
designed within the mechanism, which contains 6 types of medical-related
information, including department, drug, check, symptom, disease, food.
Besides, the specific token concatenation policy is defined to effectively
inject medical information into the input data. Evaluation of our method is
carried out on two typical medical datasets, MedDG and MedDialog-CN. The
evaluation results demonstrate that models combined with our mechanism
outperform original methods in multiple automatic evaluation metrics. Besides,
MKA-Bert-GPT achieves state-of-the-art performance. The open-sourced codes are
public:
https://github.com/LIANGKE23/Knowledge_Assisted_Medical_Dialogue_Generation_Mechanism