User mental health monitoring system and method based on heterogeneous graph

Through a user mental health monitoring system based on heterogeneous graphs, using social media data and SAM scale information, and using a neural network model with hierarchical aggregation and a two-layer attention mechanism, the problem of difficulty in identification and monitoring in traditional mental health diagnosis and treatment methods is solved, and a more comprehensive user mental health monitoring effect is achieved.

CN119092113BActive Publication Date: 2025-03-21SECOND MEDICAL CENT OF CHINESE PLA GENERAL HOSPITAL
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Patent Information

Application Number
CN202411057971.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-08-02
Publication Date
2025-03-21
Estimated Expiration
2044-08-02

AI Technical Summary

Technical Problem

Traditional mental health diagnosis and treatment methods require face-to-face communication, limiting the possibility of potential patients' identification, and it is difficult for patients to conduct effective mental health monitoring and tracking due to economic, resource, social discrimination and privacy protection.

Method used

The user mental health monitoring system based on heterogeneous graphs uses a heterogeneous graph neural network model with a hierarchical aggregation method and a two-layer attention mechanism to aggregate emotional nodes and fusion of adjacent nodes to judge the user's multiple emotions and the relationship factors that cause the emotion, and check it through the normal psychological three-dimensional reference table to obtain the user's mental health monitoring results.

Benefits of technology

The data diversity and effectiveness of mental health monitoring are optimized, and the user's mental health status can be more comprehensively judged, solving the problems of difficulty in identifying and monitoring in traditional methods.

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Abstract

The present invention discloses a user mental health monitoring system and method based on a heterogeneous graph, and belongs to the field of psychological monitoring technology. The present invention solves the problem of poor effectiveness of existing methods, collects user social information, and adopts a hierarchical aggregation method to aggregate and classify images and texts to obtain multiple emotional nodes and adjacent node parameters; constructs a heterogeneous graph neural network model based on a double-layer attention mechanism to aggregate different emotional nodes again, and then fuses adjacent nodes to obtain a more comprehensive current user emotional aggregation result, which is convenient for judging the user's multiple emotions and the related factors that cause the emotions; creates a normal psychological three-dimensional reference table, checks the current user's emotions and the factors belonging to them according to the psychological three dimensions, and judges a more comprehensive user mental health monitoring result by analyzing the pleasure, arousal and control of the current user's emotions, thereby optimizing the effectiveness of the existing mental health monitoring method.
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