Mental health big data early warning and decision support system
By constructing a big data early warning and decision support system for mental health, integrating multi-dimensional data collection and analysis, calculating individual mental health indices, and setting multi-level thresholds, the system solves the problem of insufficient assessment of complex psychological conditions in existing early warning systems, realizes full-cycle management and early risk identification, and improves the scientificity and efficiency of mental health management.
Patent Information
- Application Number
- CN202510931616.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-07
- Publication Date
- 2025-10-31
AI Technical Summary
Existing early warning systems mostly rely on simple behavioral or emotional indicators, lacking a multi-dimensional comprehensive assessment of complex psychological states, resulting in insufficient early warning sensitivity.
A big data early warning and decision support system for mental health is constructed, including a multi-dimensional data collection module, an individual data analysis module, an early warning module, and a decision support module. By integrating individual background, behavioral status, and physiological indicator data, an individual mental health index is calculated, and multi-level thresholds are set to trigger graded early warnings and differentiated interventions.
It enables full-cycle management of mental health, enhances the sensitivity and scientific rigor of early warning systems, reduces the risk of mental health problems worsening, and provides comprehensive mental health protection.
Smart Images
Figure CN120878081A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of mental health technology, specifically a mental health big data early warning and decision support system. Background Technology
[0002] Mental health is not simply the absence of mental illness. The World Health Organization defines it as "a state of well-being in which an individual recognizes his or her own capabilities, is able to cope with normal stresses in life, is able to work productively and contribute to his or her community." It encompasses multiple aspects, including cognition, emotion, behavior, and social adaptation. It is a dynamic equilibrium state achieved through the interaction of physiological, psychological, and social environments. Mental health is a dynamic and continuously evolving process. Individuals may face various psychological challenges at different stages of life and in different living environments. Understanding relevant knowledge about mental health and mastering methods for maintaining mental health are of great significance for improving quality of life, promoting personal growth, and fostering harmonious social development.
[0003] Existing early warning systems rely heavily on simple behavioral or emotional indicators and lack a multi-dimensional comprehensive assessment of complex psychological conditions, resulting in insufficient early warning sensitivity. Summary of the Invention
[0004] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a mental health big data early warning and decision support system. This system integrates multi-dimensional data collection, individual data analysis, tiered early warning, and differentiated decision support to construct a full-cycle management system covering monitoring, assessment, early warning, and intervention. This effectively reduces the risk of worsening mental health problems, enhances the scientific rigor, relevance, and efficiency of mental health management, provides comprehensive mental health protection for individuals and society, and comprehensively improves early warning sensitivity.
[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a mental health big data early warning and decision support system, comprising a multi-dimensional data acquisition module, an individual data analysis module, an early warning module, and a decision support module; The multidimensional data acquisition module includes a personal characteristic data acquisition unit, a behavioral state data acquisition unit, and a physiological indicator data acquisition unit. The personal characteristic data acquisition unit is used to acquire personal background data, the behavioral state data acquisition unit is used to acquire personal behavioral state data, and the physiological indicator data acquisition unit is used to acquire personal physiological indicator data. The multidimensional data acquisition module sends the acquired data to the personal data analysis module via a network. The individual data analysis module is used to evaluate the individual background influence index and the individual psychological state index based on the data obtained by the multidimensional data acquisition module, and to calculate the individual mental health index by combining the individual background influence index and the individual psychological state index. The individual data analysis module sends the calculated individual mental health index to the early warning module via the network. The early warning module performs a mental health assessment based on the individual's mental health index calculation results and sends the assessment results to the decision support module. The decision support module is used to make corresponding decision responses based on the received evaluation results.
[0006] Preferably, the individual background data Including historical average mental health index Basic Disease Characteristic Index Life event impact index .
[0007] Preferably, the individual behavioral state data Including sleep quality Activity level Dietary behavior Social frequency Interest and participation negative emotions Abnormal behavior Concentration .
[0008] Preferably, the individual physiological index data Including neurological indicators Endocrine system indicators Immune system indicators Metabolic indicators Brain function and neural activity .
[0009] Preferably, the individual background influence index The calculation formula is: In the formula, The index represents the influence of individual background. Represents the historical average mental health index The weight, Represents basic disease characteristics index The weight, Representative Life Event Impact Index The weight.
[0010] Preferably, the individual psychological state index The calculation formula for the assessment is as follows: In the calculation formula, This represents an individual's psychological state index. Represents behavioral characteristic index. The weights representing behavioral characteristic indices. Represents physiological characteristic index. The weights of the physiological characteristic index.
[0011] Preferably, the behavioral characteristic index The calculation formula is: In the calculation formula, Represents behavioral characteristic index. The first in the data representing individual behavioral status One parameter, Representing the The mean of each parameter, Representing the The standard deviation of each parameter Representing the The weights of each parameter.
[0012] Preferably, the physiological characteristic index The calculation formula is: In the calculation formula, Represents physiological characteristic index. The first in the data representing individual behavioral status One parameter, Representing the The mean of each parameter, Representing the The standard deviation of each parameter Representing the The weights of each parameter.
[0013] Preferably, the individual mental health index The calculation formula is: In the calculation formula, Represents an individual's mental health index. Individual background influence index The weight, The weights representing an individual's psychological state index.
[0014] Preferably, the early warning module is internally configured with a low threshold, a medium threshold, and a high threshold for the individual mental health index; When the value of the individual's mental health index is lower than the lower threshold of the individual's mental health index, it indicates that there are no mental health problems. When the value of the individual's mental health index is higher than the low threshold of the individual's mental health index but lower than the medium threshold of the individual's mental health index, it indicates that there is a low-level mental health problem, and a low-level warning is sent. When the value of the individual's mental health index is higher than the intermediate threshold but lower than the advanced threshold, it indicates that there is an intermediate mental health problem, and an intermediate warning is sent. When the value of the individual's mental health index is higher than the advanced threshold of the individual's mental health index, it indicates that there is an advanced mental health problem, and an advanced warning is sent. The decision support module conducts regular monitoring for those without mental health issues, maintains routine data collection, and pays attention to potential risk factors. The decision support module automatically sends care messages to address low-level mental health issues, recommends online psychological counseling, shortens the monitoring cycle, and focuses on indicator fluctuations. The decision support module assigns a dedicated psychological consultant to intermediate-level mental health issues, conducts one-on-one assessments, and collaborates with medical institutions to screen for somatization symptoms. The decision support module triggers a red alert for advanced mental health issues, activates an emergency protocol, and monitors behavioral and physiological data daily.
[0015] Compared with existing technologies, this invention provides a big data early warning and decision support system for mental health, which has the following beneficial effects: This invention integrates multi-dimensional data collection, individual data analysis, tiered early warning, and differentiated decision support to construct a full-cycle management system covering monitoring, assessment, early warning, and intervention. Based on multi-dimensional data fusion of individual background, behavioral status, and physiological indicators, combined with formulaic calculations, it achieves accurate assessment of mental health indices. By setting multi-level thresholds to trigger tiered early warnings and linking differentiated intervention measures, it ensures early risk identification and optimal resource allocation. Simultaneously, through dynamic monitoring and collaborative linkage with medical institutions and expert resources, the system forms a scientific decision-making closed loop, effectively reducing the risk of mental health problems worsening, improving the scientific nature, pertinence, and efficiency of mental health management, and providing comprehensive mental health protection for individuals and society. Attached Figure Description
[0016] Figure 1 This is a schematic diagram of the system of the present invention. Detailed Implementation
[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0018] Please see Figure 1 The mental health big data early warning and decision support system includes a multi-dimensional data acquisition module, an individual data analysis module, an early warning module, and a decision support module. The multidimensional data acquisition module includes a personal characteristic data acquisition unit, a behavioral state data acquisition unit, and a physiological indicator data acquisition unit. The personal characteristic data acquisition unit is used to acquire individual background data, the behavioral state data acquisition unit is used to acquire individual behavioral state data, and the physiological indicator data acquisition unit is used to acquire individual physiological indicator data. The multidimensional data acquisition module sends the acquired data to the individual data analysis module via the network. Individual background data Including historical average mental health index Basic Disease Characteristic Index Life event impact index ; Individual behavioral status data Including sleep quality Activity level Dietary behavior Social frequency Interest and participation negative emotions Abnormal behavior Concentration ; Individual physiological indicators data Including neurological indicators Endocrine system indicators Immune system indicators Metabolic indicators Brain function and neural activity ; The monitoring data covers three main categories: individual background, behavioral status, and physiological indicators. Individual background data can reflect an individual's long-term psychological state and potential risk factors, helping to identify the root causes of mental health problems. Individual behavioral status data can reflect an individual's daily behavioral patterns and psychological state in real time, helping to detect early abnormal behaviors or emotional fluctuations. Individual physiological indicator data can reveal potential changes in mental health from a physiological perspective, providing a scientific basis for early warning and intervention of mental health problems. The comprehensive analysis of these monitoring data can comprehensively improve the accuracy of mental health assessment, realize early warning, personalized intervention, and precise decision support, thereby effectively maintaining and promoting individual mental health. The individual data analysis module is used to assess the individual background influence index and the individual psychological state index based on the data obtained by the multidimensional data acquisition module. It also calculates the individual mental health index by combining the individual background influence index and the individual psychological state index assessment. The individual data analysis module sends the calculated individual mental health index to the early warning module via the network. Individual background influence index The calculation formula is: In the formula, The index represents the influence of individual background. Represents the historical average mental health index The weight, Represents basic disease characteristics index The weight, Representative Life Event Impact Index The weights; Individual Psychological State Index The calculation formula for the assessment is as follows: In the calculation formula, This represents an individual's psychological state index. Represents behavioral characteristic index. The weights representing behavioral characteristic indices. Represents physiological characteristic index. The weights representing physiological characteristic indices; Behavioral Characteristic Index The calculation formula is: In the calculation formula, Represents behavioral characteristic index. The first in the data representing individual behavioral status One parameter, Representing the The mean of each parameter, Representing the The standard deviation of each parameter Representing the The weights of each parameter; Physiological characteristic index The calculation formula is: In the calculation formula, Represents physiological characteristic index. The first in the data representing individual behavioral status One parameter, Representing the The mean of each parameter, Representing the The standard deviation of each parameter Representing the The weights of each parameter; Individual mental health index The calculation formula is: In the calculation formula, Represents an individual's mental health index. Individual background influence index The weight, The weights representing an individual's psychological state index; The individual data analysis module integrates background, behavioral, and physiological data acquired by the multidimensional data collection module. It uses a formulaic assessment method to calculate the individual's background influence index, psychological state index, and mental health index, enabling multidimensional and personalized mental health assessment. This not only improves the accuracy and scientific rigor of the assessment but also identifies potential risks through early warning, providing a basis for intervention. Furthermore, by combining weighted parameters and dynamic monitoring, it supports personalized decision-making and real-time management. Finally, the module transmits the assessment results to the early warning module via a network, forming a complete mental health management system that effectively promotes early detection, precise intervention, and scientific decision-making regarding mental health issues. The early warning module has three thresholds: a low threshold, a medium threshold, and a high threshold for the individual mental health index. When an individual's mental health index value is below the lower threshold of the individual's mental health index, it indicates that there are no mental health problems. When an individual's mental health index value is higher than the lower threshold but lower than the middle threshold, it indicates the presence of a low-level mental health problem, and a low-level warning is issued. When an individual's mental health index value is higher than the intermediate threshold but lower than the advanced threshold, it indicates the presence of an intermediate mental health problem, and an intermediate warning is issued. When an individual's mental health index value is higher than the advanced threshold, it indicates that there is an advanced mental health problem, and an advanced warning is sent. The decision support module conducts regular monitoring for individuals without mental health issues, maintains routine data collection, and focuses on potential risk factors. The decision support module automatically sends care messages to address low-level mental health issues, recommends online psychological counseling, shortens the monitoring cycle, and monitors indicator fluctuations. The decision support module assigns a dedicated mental health consultant to intermediate-level mental health issues for one-on-one assessment and collaborates with medical institutions to screen for somatization symptoms. The decision support module triggers a red alert for advanced mental health issues, activates emergency protocols, and monitors behavioral and physiological data daily. The early warning module enables graded assessment and precise early warning of individual mental health status by setting multi-level mental health index thresholds. Combined with the differentiated response mechanism of the decision support module, it forms a closed-loop management system from monitoring to early warning to intervention. Based on the dynamic changes in the mental health index, it can automatically trigger tiered management measures such as regular monitoring when there are no problems, online care for low-level problems, professional intervention for intermediate problems, and emergency response for high-level problems. This ensures accurate identification and timely intervention of early risks and optimizes resource allocation efficiency. By linking resources from medical institutions, mental health experts, and other parties, the system can achieve full-cycle dynamic tracking of individual mental health, effectively reducing the risk of mental health problems worsening, improving the scientific, targeted, and collaborative nature of mental health management, and providing comprehensive mental health protection for individuals and society.
[0019] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A mental health big data early warning and decision support system, characterized by: It includes a multi-dimensional data acquisition module, an individual data analysis module, an early warning and alert module, and a decision support module; The multidimensional data acquisition module includes a personal characteristic data acquisition unit, a behavioral state data acquisition unit, and a physiological indicator data acquisition unit. The personal characteristic data acquisition unit is used to acquire personal background data, the behavioral state data acquisition unit is used to acquire personal behavioral state data, and the physiological indicator data acquisition unit is used to acquire personal physiological indicator data. The multidimensional data acquisition module sends the acquired data to the personal data analysis module via a network. The individual data analysis module is used to evaluate the individual background influence index and the individual psychological state index based on the data obtained by the multidimensional data acquisition module, and to calculate the individual mental health index by combining the individual background influence index and the individual psychological state index. The individual data analysis module sends the calculated individual mental health index to the early warning module via the network. The early warning module performs a mental health assessment based on the individual's mental health index calculation results and sends the assessment results to the decision support module. The decision support module is used to make corresponding decision responses based on the received evaluation results.
2. The mental health big data early warning and decision support system according to claim 1, characterized in that: The individual background data Including historical average mental health index Basic Disease Characteristic Index Life event impact index .
3. The mental health big data early warning and decision support system according to claim 2, characterized in that: The individual behavioral status data Including sleep quality Activity level Dietary behavior Social frequency Interest and participation negative emotions Abnormal behavior Concentration .
4. The mental health big data early warning and decision support system according to claim 3, characterized in that: The individual physiological index data Including neurological indicators Endocrine system indicators Immune system indicators Metabolic indicators Brain function and neural activity .
5. The mental health big data early warning and decision support system according to claim 4, characterized in that: The individual background influence index The calculation formula is: ； In the formula, The index represents the influence of individual background. Represents the historical average mental health index The weight, Represents basic disease characteristics index The weight, Representative Life Event Impact Index The weight.
6. The mental health big data early warning and decision support system according to claim 5, characterized in that: The individual psychological state index The calculation formula for the assessment is as follows: ； In the calculation formula, This represents an individual's psychological state index. Represents behavioral characteristic index. The weights representing behavioral characteristic indices. Represents physiological characteristic index. The weights of the physiological characteristic index.
7. The mental health big data early warning and decision support system according to claim 6, characterized in that: The behavioral characteristic index The calculation formula is: ； In the calculation formula, Represents behavioral characteristic index. The first in the data representing individual behavioral status One parameter, Representing the The mean of each parameter, Representing the The standard deviation of each parameter Representing the The weights of each parameter.
8. The mental health big data early warning and decision support system according to claim 7, characterized in that: The physiological characteristic index The calculation formula is: ； In the calculation formula, Represents physiological characteristic index. The first in the data representing individual behavioral status One parameter, Representing the The mean of each parameter, Representing the The standard deviation of each parameter Representing the The weights of each parameter.
9. The mental health big data early warning and decision support system according to claim 8, characterized in that: The individual mental health index The calculation formula is: ； In the calculation formula, Represents an individual's mental health index. Individual background influence index The weight, The weights representing an individual's psychological state index.
10. The mental health big data early warning and decision support system according to claim 9, characterized in that: The early warning module is internally configured with a low threshold, a medium threshold, and a high threshold for the individual mental health index. When the value of the individual's mental health index is lower than the lower threshold of the individual's mental health index, it indicates that there are no mental health problems. When the value of the individual's mental health index is higher than the low threshold of the individual's mental health index but lower than the medium threshold of the individual's mental health index, it indicates that there is a low-level mental health problem, and a low-level warning is sent. When the value of the individual's mental health index is higher than the intermediate threshold but lower than the advanced threshold, it indicates that there is an intermediate mental health problem, and an intermediate warning is sent. When the value of the individual's mental health index is higher than the advanced threshold of the individual's mental health index, it indicates that there is an advanced mental health problem, and an advanced warning is sent. The decision support module conducts regular monitoring for those without mental health issues, maintains routine data collection, and pays attention to potential risk factors. The decision support module automatically sends care messages to address low-level mental health issues, recommends online psychological counseling, shortens the monitoring cycle, and focuses on indicator fluctuations. The decision support module assigns a dedicated psychological consultant to intermediate-level mental health issues, conducts one-on-one assessments, and collaborates with medical institutions to screen for somatization symptoms. The decision support module triggers a red alert for advanced mental health issues, activates an emergency protocol, and monitors behavioral and physiological data daily.