Heart failure patient remote monitoring and early warning system based on Internet of Things

Through the Internet of Things combined with extreme gradient enhancement tree algorithm and recursive feature elimination method, a remote monitoring system for heart failure patients was built, which solved the data lag and model stability problems in heart failure risk assessment, and achieved efficient and accurate risk warning and dynamic optimization.

CN120376195AActive Publication Date: 2025-07-25THE FIRST AFFILIATED HOSPITAL OF MEDICAL COLLEGE OF XIAN JIAOTONG UNIV
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Patent Information

Application Number
CN202510865988.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-26
Publication Date
2025-07-25
Estimated Expiration
2045-06-26

AI Technical Summary

Technical Problem

The prior art has data lag, slow response, poor model stability, and timely feature redundancy in the assessment of heart failure risk, lack of dynamic optimization mechanisms, making it difficult to adapt to patient status changes and individual differences.

Method used

A remote monitoring system for heart failure patients based on the Internet of Things is adopted, and an extreme gradient enhancement tree algorithm and recursive feature elimination method is used to build a multi-level early warning mechanism through feature importance assessment, feature screening, risk scores and dynamic feedback optimization, combining graph structure embedding and adaptive learning rate regulation to achieve continuous optimization and efficient screening of the model.

Benefits of technology

It significantly improves the accuracy and response timeliness of heart failure risk assessment, is adaptable and robust, can be continuously adjusted in long-term deployment scenarios, and is suitable for remote health management.

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Abstract

The invention discloses a heart failure patient remote monitoring and early warning system based on the Internet of Things, and the system comprises a data collection and preprocessing module which is used for collecting and preprocessing multi-source physiological and behavior data, and generating an input sample set; the feature evaluation module is used for constructing an extreme gradient boosting tree algorithm model and obtaining feature importance scores; the feature screening module is used for executing a recursive feature elimination process and outputting an optimal feature subset; the risk judgment module is used for inputting the optimal feature subset into an extreme gradient boosting tree algorithm model to generate a heart failure risk score; the early warning triggering module is used for triggering early warning and sending early warning information to the patient and the platform; the feedback recording module is used for generating feedback information; and the dynamic optimization module is used for updating the input sample set based on the feedback information, retraining the extreme gradient boosting tree algorithm model and screening the features. According to the method, the extreme gradient boosting tree and recursive feature elimination are fused, and accurate evaluation and dynamic early warning of the multi-source data of the heart failure patient are realized.
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