Intelligent inquisition method, system, computer equipment and storage medium

Abstract

The invention discloses an intelligent inquisition method, an intelligent inquisition system, computer equipment and a storage medium. The intelligent inquisition method includes the following steps that: disease prior probability prediction is performed according to the main complaint content of a target object, disease prior probabilities for the target object are obtained; at least one candidate disease is determined based on the order of the disease prior probabilities; and at least one agent strategy correspondingly associated with the at least one candidate disease is determined from a pre-established multi-agent model based on reinforcement learning, wherein each agent strategy is used to indicate questions which are required to be raised for the output of next interaction under a current symptomatic state; based on each agent strategy, interactive consultation is conducted according to the interactive selection of the questions which are required to be raised for the output ofthe next interaction for the target object. With the intelligent inquisition method, the intelligent inquisition system, the computer equipment and the storage medium of the invention adopted, the rationality of inquisition logic for related diseases and user experience can be optimized with the accuracy of inquisition results ensured.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to an intelligent consultation method, system, computer equipment and computer-readable storage medium. Background technique [0002] At present, in the field of medical intelligent consultation, there are mainly the following two types of methods to realize intelligent consultation: one is based on the traditional Probabilistic Graphical Model (PGM) and Information Gain (Information Gain) method, which can Appropriately simulate the consultation process and give a disease diagnosis; the other is a relatively new deep learning model in recent years, which diagnoses the current possible disease based on the observed symptoms of the patient. Commonly used methods based on deep learning models are networks based on DNN (Deep Neural Networks, deep neural network), CNN (Convolution Neural Network, convolutional neural network), LSTM (Long Short-Term Memory, long-term shor...

AI Technical Summary

Problems solved by technology

[0003] However, the above-mentioned traditional methods based on probabilistic graphical model (PGM) and information gain (Information Gain) often have relatively high computational complexity in the inference part of the model. At the same time, the disease-symptom transition matrix of the probabilistic graphical model often requires professional Doctor labeling, a model similar to an expert system, faces great challenges in disease expansion

In addition, the above-mentioned intelligent consultation method based on the deep learning model can give a relatively high accuracy rate of disease prediction when all the observed symptoms are known, but usually, we cannot observe all the symptoms of the patient , the model based on deep learning is difficult to give a reasonable consultation process due to its inexplicability

Images