Dynamic blood glucose rule engine driven abnormal pattern automatic labeling system and method

The rule engine-driven automatic annotation method for abnormal patterns solves the problems of low efficiency, low accuracy, and inconsistent standards in manual annotation of dynamic blood glucose monitoring data, achieving efficient and accurate automatic annotation and promoting the development of precision diabetes management technology.

CN122025193BActive Publication Date: 2026-07-24ANHUI PROVINCIAL HOSPITAL +1
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Patent Information

Application Number
CN202610490910.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-04-14
Publication Date
2026-07-24
Estimated Expiration
2046-04-14

AI Technical Summary

Technical Problem

In existing technologies, manual annotation of dynamic blood glucose monitoring data is inefficient, has low accuracy, and inconsistent standards, resulting in poor quality training data for AI models, which makes it difficult to meet the needs of large-scale, multi-center intelligent diabetes management.

Method used

An automatic anomaly pattern labeling method driven by a rule engine is adopted. By acquiring dynamic blood glucose monitoring data and related multi-dimensional information, format verification and preliminary integration are performed. After preprocessing, the analysis time window is automatically determined based on a preset abnormal blood glucose pattern recognition rule library, anomaly patterns are identified, and structured labeled data is generated.

Benefits of technology

It achieves efficient automatic annotation of dynamic blood glucose data, improving efficiency by a hundredfold and achieving an annotation accuracy of over 98%, ensuring the consistency and reliability of annotation results, supporting large-scale parallel data processing, and the output annotation data can be directly used for AI large model training.

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Abstract

The application discloses a dynamic blood glucose rule engine driven abnormal mode automatic labeling system and method, comprising: acquiring dynamic blood glucose monitoring data and associated multi-dimensional information and integrating; preprocessing data to obtain standardized data; determining an analysis window based on a rule base containing four types of core parameters; identifying an abnormal mode through a universal logic: defining a window, identifying a characteristic event, calculating an associated parameter, and multiple determinations; positioning an anomaly and associating a trigger factor to generate labeling information containing a unique identifier; outputting structured data, a clinical report and a log. The system corresponds to a four-layer architecture including data input, a rule engine, a processing engine and output. The application realizes automatic and accurate labeling of blood glucose abnormalities, supports batch processing, greatly reduces the labor and time cost of blood glucose abnormal labeling, provides a data source for rapid iteration and optimization of artificial intelligence big models, and improves blood glucose management efficiency.
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