Ophthalmology data analysis method and device based on crowd queue analysis and medium
By constructing Bayesian decision network and performing recursive selection and clustering, the complexity and efficiency of ophthalmic data analysis are solved, and high accuracy and high efficiency of ophthalmic data analysis are achieved.
CN119742077AActive Publication Date: 2025-04-01GUANGDONG DIGITAL MEDICAL TECH CO LTD
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Patent Information
- Application Number
- CN202411557139.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-04
- Publication Date
- 2025-04-01
- Estimated Expiration
- 2044-11-04
AI Technical Summary
Technical Problem
The causes and related factors of ophthalmic diseases are complex, and it is difficult for the existing technology to efficiently analyze ophthalmic data, resulting in the accuracy and efficiency of the analysis results.
Method used
A Bayesian decision network is constructed by using a method based on population cohort analysis, and the target ophthalmic data analysis results are generated through recursive selection and clustering processing.
Benefits of technology
It improves the accuracy and analysis efficiency of ophthalmic data analysis results, can automatically analyze ophthalmic related data, and provides effective auxiliary diagnostic information.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses an ophthalmology data analysis method and device based on crowd queue analysis, and a medium. The method comprises the following steps: constructing a first Bayesian decision network based on ophthalmology associated data; calculating a Simpson index between the reference nodes in each initial community, and performing recursive selection on each node of the first Bayesian decision network based on the Simpson index and a preset probability threshold to obtain a second Bayesian decision network; clustering the intermediate community of the second Bayesian decision network to obtain a target Bayesian decision network; and generating a target ophthalmology data analysis result based on the variable values of the nodes of each target community in the target Bayesian decision network and the conditional dependency relationship between the nodes. The ophthalmology related data is automatically analyzed through crowd queue analysis, the target ophthalmology data analysis result is obtained, and compared with a manual analysis scheme, the accuracy of the ophthalmology data analysis result is improved, and the analysis efficiency is improved.
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