Medical consultation dialogue system and method applying heterogeneous graph neural network

Abstract

The invention discloses a medical consultation dialogue system and method applying a heterogeneous graph neural network. The system comprises a dialogue history coding module, a medical entity prediction module and a graph guide dialogue generation module, wherein the dialogue history coding module carries out the hierarchical coding of a dialogue history through a neural network model, and obtains the feature vector representation of each statement and the whole dialogue history; the medical entity prediction module is used for constructing a heterogeneous graph containing medical entity nodes and statement information nodes according to the medical knowledge graph and the dialogue history, initializing the statement information nodes in the heterogeneous graph according to the obtained encoding vectors, spreading current information to related entity nodes on the heterogeneous graph by using a graph attention network, predicting symptoms or disease entities which may be inquired by doctors in the next round of dialogue; and the graph guide dialogue generation module is used for dynamically selecting and generating words from a common dialogue word list or using medical entity expression of related nodes of a heterogeneous graph according to the current state of the dialogue and the reasoning result of the heterogeneous graph, so that a more accurate and effective reply containing professional terms is generated.

Description

technical field [0001] The invention relates to the field of medical information technology, in particular to a medical consultation dialogue system and method using a heterogeneous graph neural network. Background technique [0002] Difficulty in seeing a doctor has always been the most prominent problem in my country's medical system. With the development of big data and the Internet, people began to complete the initial diagnosis of diseases through search engines and online consultation. However, the search engine can only search for the Q&A results of similar cases. Due to the lack of medical common sense and judgment ability of users, these biased results often lead to wrong cognition. On the other hand, there are many problems such as low communication efficiency, high fees, and uneven quality of doctors in online medical consultation, making it difficult for users to obtain satisfactory diagnosis results. Therefore, it is an urgent need to build a dialogue system t...

AI Technical Summary

Problems solved by technology

The existing system lacks dynamic interaction with patients and guides patients to describe their situation more

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