Medical interrogation dialogue system and reinforcement learning method applied to medical interrogation dialogue system

Abstract

The invention discloses a medical interrogation dialogue system and a reinforcement learning method applied to the medical interrogation dialogue system, and relates to the technical field of medicalinformation. The system comprises a natural language understanding module used for classifying the intentions of users and filling slot values to form structured semantic frames; a dialogue managementmodule used for interacting with a user through a robot agent, inputting a dialogue state, performing action decision on the semantic frame through a decision network, and outputting final system action selection; a user simulator used for carrying out natural language interaction with the dialogue management module and outputting user action selection; a natural language generation module used for receiving system action selection and user action selection, enabling the user to check the selection through generating sentences similar to a human language by using a template-based method. According to the invention, the medical knowledge information between diseases and symptoms is introduced as a guide, and the inquiry historical experience is enriched through continuous interaction witha simulated patient. The reasonability of inquiry symptoms and the accuracy of disease diagnosis are improved, and the diagnosis result is higher in credibility.

Description

technical field [0001] The present invention relates to the field of medical information technology, and more specifically relates to a medical consultation dialogue system and a reinforcement learning method applied to the system. Background technique [0002] Difficulty in seeing a doctor has always been the most prominent problem in my country's medical system. The essence is that the doctor-patient ratio is too low. my country is the most populous country in the world, and medical care is the most basic need of people's life. However, with such a large population, seeing a doctor Difficulty naturally becomes an acute question. Ordinary people often have to wait an hour or two to see a doctor when they have a fever and a cold, and then come out after a few words with the doctor, so even a minor illness takes half a day, but the actual diagnosis takes only a few minutes. With the development of big data and the Internet, people use search engines to complete initial self-d...

AI Technical Summary

Problems solved by technology

[0011] The purpose of the present invention is: in order to solve the problem that the existing consultation system extracts keywords through natural language understanding technology, and then obtains diagnostic opinions through keyword matching, but the interpretability of the results obtained based on keyword matching is not strong, and the matching results may be wrong problem, the present invention provides a medical consultation dialogue system and a reinforcement learning method applied to the system. This method can effectively introduce medical knowledge information between diseases and symptoms as a guide, and at the same time, it can Interaction enriches one's own history of consultation experience, improves the rationality of asking symptoms and the accuracy of diagnosing diseases, making the diagnosis results obtained by the system more reliable

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