Personalized medical health service recommendation system based on big data analysis

By synchronizing the dynamic blood glucose monitoring data and diet logs in minute-level timestamps, combining wavelet transformation and pharmacokinetic models, using recurrent neural networks to fusion, personalized service recommendations are generated, and the problem of bias of recommended services and physiological state in the existing system is solved, and accurate prediction and timely intervention of hypoglycemia risk are achieved.

CN120496834APending Publication Date: 2025-08-15CHINESE PEOPLES LIBERATION ARMY GENERAL HOSPITAL HAINAN HOSPITAL
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Patent Information

Application Number
CN202510567630.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-30
Publication Date
2025-08-15

AI Technical Summary

Technical Problem

The existing medical and health service recommendation system fails to make full use of patients' dynamic monitoring information, resulting in significant deviations from the patient's physiological status in diabetes management, especially when the risk of asymptomatic hypoglycemia is not possible.

Method used

By obtaining dynamic blood glucose monitoring data, insulin injection records and diet log text data, minute-level timestamp synchronization processing is performed, and standardized data flow is generated by combining wavelet transformation and pharmacokinetic models. Recurrent neural networks are used for feature fusion and risk assessment, and personalized service recommendations are generated.

Benefits of technology

A minute-level accurate prediction and personalized intervention for the risk of hypoglycemia are achieved, reducing the deviation between the recommended service and the actual physiological status, and ensuring the timeliness of clinically acceptable interventions in asymptomatic hypoglycemia scenarios.

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Abstract

The invention discloses a personalized medical health service recommendation system based on big data analysis, and belongs to the technical field of medical health services. The synchronization module is used for generating a standardized data stream according to dynamic blood glucose monitoring data and an original heart rate variability signal; the analysis module is used for generating a diet time sequence data stream according to the intake event timestamp; the blood glucose feature extraction module is used for extracting data from the standardized data stream; the insulin sensitivity analysis module is used for generating an insulin sensitivity factor matrix; the nutrition analysis module is used for generating a carbohydrate equivalent time distribution vector and a dietary fiber intake intensity value; the feature fusion module is used for generating a fusion feature vector according to the blood glucose drift trend coefficient, the insulin sensitive factor matrix and the carbohydrate equivalent time distribution vector; the risk assessment module is used for generating a hypoglycemia risk probability value according to the fusion feature vector; and the recommendation generation module is used for generating a personalized service recommendation set according to the hypoglycemia risk probability value.
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