Diabetes abnormal index early warning system and method based on recurrent neural network

By integrating multiple health monitoring devices and convolutional gated recurrent neural networks for comprehensive analysis, the systemic and real-time deficiencies in diabetes management in existing technologies have been addressed, enabling comprehensive monitoring and personalized early warning for diabetic patients, and improving prediction accuracy and management effectiveness.

CN121354884APending Publication Date: 2026-01-16DAITE INTELLIGENT TECH (SHANGHAI) CO LTD
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Patent Information

Application Number
CN202410946833.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-07-16
Publication Date
2026-01-16

AI Technical Summary

Technical Problem

Existing technologies lack a systematic and comprehensive approach to diabetes management. Traditional blood glucose monitoring methods cannot fully reflect the patient's health status. Data from various physiological indicators monitored by smart devices exist in isolation. Machine learning algorithms suffer from insufficient accuracy and poor real-time performance when processing large-scale, multi-dimensional time-series data.

Method used

A diabetes abnormality indicator early warning system based on recurrent neural networks is adopted. It integrates multiple health monitoring devices to collect various physiological indicator data in real time, and performs comprehensive analysis through convolutional gated recurrent neural networks to generate personalized early warning information, including data acquisition, preprocessing, feature extraction and early warning generation.

Benefits of technology

It enables comprehensive monitoring and real-time early warning of diabetic patients, improves the accuracy of predicting abnormal diabetes indicators, provides personalized health management suggestions, and enhances the effectiveness of diabetes management.

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Abstract

The invention provides a diabetes abnormal index early warning system and method based on a recurrent neural network. The system comprises a data acquisition module which is responsible for collecting various health data including blood sugar, heart rate, blood pressure, body weight, body fat, exercise amount, sleep quality and heart rate variability from health monitoring equipment of a patient; the data preprocessing module is used for denoising and normalizing the collected health data and detecting and correcting abnormal values; the feature extraction module is used for extracting time sequence features, statistical features and principal component features from the preprocessed health data; the convolutional gating recurrent neural network model module is used for analyzing the extracted features; and the early warning generation module generates early warning information according to the prediction result. Through the system and the method, the real-time monitoring of the multi-dimensional health data of the diabetic patient and the early warning of the abnormal indexes can be realized, and the diabetes management effect and the life quality of the patient can be improved.
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