Migratable medical inquiry dialogue system and method for low-resource scene

Abstract

The invention discloses a migratable medical inquiry dialogue system and method for a low-resource scene, and the system comprises: a simulation patient construction module which is used for buildinga simulation patient which is provided with a plurality of dialogue samples, randomly selects one dialogue sample for each training, and gives an illness state description report for the simulation patient; an intelligent medical inquiry system construction module which is used for establishing an intelligent medical inquiry system, extracting context information of different hierarchies from dialogue history through the layered context encoder module according to disease description, encoding to obtain feature vectors of single-round hierarchies and multi-round hierarchies of dialogues, and establishing an intelligent medical inquiry system by evolving an external medical knowledge graph, relationship reasoning between disease symptoms is performed according to the feature vectors to obtain graph node feature vectors to generate replies of doctors to patients by utilizing a replication network under the guidance of graph node information; and a training module which is used for carrying out end-to-end training on the system and training the system on dialogue data of existing diseases by utilizing a graph evolution meta-learning algorithm.

Description

technical field [0001] The invention relates to the field of medical information technology, in particular to a medical consultation dialogue system and method that can be migrated to low-resource scenarios. 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 use search engines to complete initial self-diagnosis of diseases. However, what search engines search for are the question-and-answer results of relevant cases that exist on the Internet. The results of this diagnosis may be completely consistent with users. Symptoms lead to deviations or even errors in diagnosis results. The quality of doctors on existing offline consultation websites is uneven, and some doctors may give wrong diagnosis results. Therefore, it is necessary to build an automatic diagnosis dialogue system with knowledge reasoning ability. A need that needs...

AI Technical Summary

Problems solved by technology

However, the existing medical consultation dialogue systems mostly rely on a large amount of dialogue data for

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