Medical consumable abnormal data type-2 fuzzy screening method based on cross-domain collaboration and isomerism map

By employing a type II fuzzy screening method for abnormal medical consumable data based on cross-domain collaboration and heterogeneous graphs, combined with NLP, cross-domain transfer learning, and graph neural networks, the problem of inconsistent features in local medical consumable data was solved, achieving efficient screening and repair of abnormal data.

CN120974390AActive Publication Date: 2025-11-18UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Patent Information

Application Number
CN202511504814.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-21
Publication Date
2025-11-18
Estimated Expiration
2045-10-21

AI Technical Summary

Technical Problem

The current discrepancy between local and national medical consumables data leads to slow processing speeds and low accuracy. Existing technologies are unable to effectively screen products with inconsistent characteristics, causing challenges to data governance.

Method used

A type II fuzzy screening method for abnormal medical consumable data based on cross-domain collaboration and heterogeneous graphs is adopted. Through a three-level governance framework combined with NLP, cross-domain transfer learning, graph neural networks and type II fuzzy systems, a deep learning model is constructed to screen and repair abnormal data.

Benefits of technology

It improves the judgment accuracy of cross-domain transfer learning, achieves high precision in anomaly screening and accurate data repair, and enhances the decision-maker's adaptability to high-dimensional dynamic data.

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Abstract

The invention discloses a medical consumable abnormal data type-2 fuzzy screening method based on cross-domain collaboration and a heterogeneous map, and belongs to the technical field of medical information processing. According to the method, professional extraction is carried out on medical consumable data features through a medical consumable attribute feature cleaning and extracting system based on a language processing model. Introducing an abnormal collaborative reasoning model based on first-class transfer learning and deep domain confrontation, and carrying out preliminary study and judgment on the distribution of data features; and in combination with a built heterogeneous semantic knowledge graph information fusion framework based on a knowledge graph and a graph neural network, carrying out deep anomaly detection and correction attempt on a product generating primary anomaly. And after the repair is completed, performing deep discrimination on the corrected abnormal product through an identification and analysis model based on type-2 fuzziness. The method is suitable for multi-dimensional information fusion, knowledge enhancement modeling, multi-section uncertainty discrimination and high-precision and extensible medical consumable data anomaly intelligent screening application requirements in a medical consumable treatment scene.
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