An individual dynamic metabolic abnormality early warning system based on functional data analysis

The individual dynamic metabolic abnormality early warning system based on function-based data analysis solves the problems of reference population selection and data collection difficulties in existing technologies, and achieves efficient and accurate assessment and early warning of metabolic abnormalities.

CN121545750BActive Publication Date: 2026-05-26HANGZHOU INST FOR ADVANCED STUDY UCAS
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Patent Information

Application Number
CN202610049327.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-01-15
Publication Date
2026-05-26
Estimated Expiration
2046-01-15

AI Technical Summary

Technical Problem

Existing technologies require healthy and non-healthy reference populations to assess individual metabolic abnormalities, and the data collection requirements are strict, which increases the difficulty and burden. Furthermore, the mismatch between data from different time points leads to inaccurate assessments.

Method used

An individual dynamic metabolic abnormality early warning system based on function-based data analysis is adopted. Through data smoothing, calculation of standard response curves and standard deviations, and conversion into a metabolic response health score using kernel functions, only one healthy reference group is required for assessment, relaxing the time point requirements and reducing the difficulty of data collection.

Benefits of technology

It improves the accuracy and flexibility of metabolic abnormality assessment, reduces the difficulty of selecting reference populations, simplifies the data collection process, and enables early warning of metabolic abnormalities at multiple levels.

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Abstract

This invention discloses an individual dynamic metabolic abnormality early warning system based on functional data analysis. It can determine whether there are abnormalities in the dynamic metabolic status of the individual being evaluated based on fasting and metabolic response data after a tolerance test, and issue an early warning if abnormalities are found. The system includes a data input module, a data analysis module, and an early warning module. The data input module is responsible for inputting data, including indicator data from the individual being evaluated and the healthy reference group at multiple time points before and after the tolerance test. The data analysis module is responsible for processing and analyzing the data and calculating the metabolic score of the individual being evaluated. The early warning module is responsible for determining whether to issue a metabolic abnormality warning by comparing the metabolic score obtained from the data analysis module with a threshold. If the metabolic score is lower than the threshold, an early warning is issued; otherwise, no warning is issued.
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Citation Information

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