Doctor-patient matching method based on Bayesian network

Abstract

The invention relates to a doctor-patient matching method based on a Bayesian network, and the method comprises the steps: S1, collecting electronic medical record data, determining disease and symptom nodes and values thereof, and enabling the disease and symptom nodes to serve as training set data; S2, constructing a disease-disease / disease-symptom self-interaction matrix, and taking the disease-disease / disease-symptom self-interaction matrix as a constraint of a Bayesian network; S3, constructing a Bayesian network model, and performing structure learning and parameter learning; S4, inputting the disease into the Bayesian network pre-diagnosis model by the patient to obtain all possible disease combinations for calculating the complication of the main disease and the complication; S5, calculating a matching index of doctors and patients; S6, constructing a doctor recommendation model based on the matching index of the doctors and the patients; and S7, obtaining the optimal allocation of the patients and the doctors according to the preference index of the patient. According to the invention, doctor-patient matching is carried out by combining the pre-diagnosis result, the doctorspecialty, the doctor workload and the patient preference, and the defect that the current doctor-patient matching technology is not accurate enough is solved.

Description

technical field [0001] The invention relates to the field of doctor-patient matching, in particular to a doctor-patient matching method based on a Bayesian network. Background technique [0002] At present, the doctor-patient matching technology that has been publicly used is mainly concentrated in the fields of intelligent guidance and doctor recommendation. For example, Tencent Ruizhi, an AI engine for intelligent guidance developed by Tencent, aims to extract rich medical knowledge from massive literature and reason about symptoms. According to the corresponding relationship between diseases and diseases, an expert system for disease pre-diagnosis is established; through the intelligent interrogation system of human interaction, the purpose of extracting patients' disease conditions is achieved; finally, doctor recommendations are made by integrating doctor's expertise information. This type of technology can give patients accurate doctor recommendations, thereby improvin...

AI Technical Summary

Problems solved by technology

[0003] In view of this, the purpose of the present invention is to provide a doctor-patient matching method based on the Bayesian network, which combines the pre-diagnosis results, doctor expertise, doctor workload and patient preference for doctor-patient matching, and solves the problem of current doctor-patient matching technology. inaccurate defect

Images