Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Doctor-patient Matching Method Based on Bayesian Network

A Bayesian network and matching method technology, applied in the field of doctor-patient matching based on Bayesian network, can solve the problem of inaccurate doctor-patient matching technology, and achieve the effect of solving the problem of inaccurate doctor-patient matching technology

Active Publication Date: 2022-06-21
FUZHOU UNIV
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Doctor-patient Matching Method Based on Bayesian Network
  • Doctor-patient Matching Method Based on Bayesian Network
  • Doctor-patient Matching Method Based on Bayesian Network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0037] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0038] Please refer to figure 1 , the present invention provides a Bayesian network-based doctor-patient matching method, comprising the following steps:

[0039] Step S1: Collect disease and symptom words in the electronic medical record data and summarize disease symptoms, determine disease and symptom nodes and their values, so as to reduce the total number of nodes, thereby obtaining data for pre-diagnosis Bayesian network training;

[0040] Step S2: construct a 'disease-disease / disease-symptom' self-interaction matrix, the value of the disease / symptom elements that are related is 1, the value of the irrelevant part is -1, and the value of the unknown part is 0; wherein: there is a relationship The relationship is transformed into the initial network structure of the Bayesian network, and the irrelevant connection is transformed into the part of the ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The present invention relates to a doctor-patient matching method based on Bayesian network, comprising: step S1: collect electronic medical record data, determine disease and symptom nodes and their values, and use them as training set data; step S2: construct 'disease-disease' / disease-symptom' self-interaction matrix, and as a constraint of the Bayesian network; step S3: construct the Bayesian network model, and perform structure learning and parameter learning; step S4: the patient enters the disease into the Bayesian network pre-diagnosis model , to obtain all possible disease combinations that calculate the main disease and complications concurrently; step S5 calculates the matching index of the doctor and the patient; step S6: constructs a doctor recommendation model based on the matching index of the doctor and the patient; Optimal distribution with physicians. The invention combines the pre-diagnosis results, doctor's expertise, doctor's workload and patient preference to perform doctor-patient matching, and solves the defect that the current doctor-patient matching technology is not accurate enough.

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] Currently, the doctor-patient matching technology that has been publicly used mainly focuses on intelligent guidance, doctor recommendation and other fields. For example, Tencent Ruizhi, an intelligent guidance AI engine developed by Tencent, aims to extract rich medical knowledge from massive literature and reason about symptoms. Correspondence with diseases, establish an expert system for disease pre-diagnosis; through the intelligent interrogation system of interpersonal interaction, to achieve the purpose of extracting patients' symptoms; finally, by integrating doctors' expertise information, make doctor recommendations. This type of technology can provide patients with accurate doctor recommendations, thereby improving the quality of medical services. It is one of the emerging ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G16H40/20G16H50/20G06K9/62
CPCG16H40/20G16H50/20G06F18/29
Inventor 李德彪陈思平
Owner FUZHOU UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com