Psychological assessment system and method for mental diseases
By developing a psychological assessment system for mental illnesses integrating data collection, psychological testing and artificial intelligence analysis, the problems of inconsistency and bias in the evaluation results in the existing technology are solved, and more accurate diagnosis and personalized treatment plans are achieved.
Patent Information
- Application Number
- CN202411272038.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-11
- Publication Date
- 2025-02-07
AI Technical Summary
In the prior art, psychological assessment of mental illness depends on the doctor's subjective judgment and experience, resulting in inconsistency and deviation of evaluation results.
A set of psychological assessment systems for mental illnesses has been developed, including data collection module, psychological testing module, artificial intelligence analysis engine, evaluation report generator, user interaction interface, cloud database and expert knowledge base, using wearable devices and mobile applications to collect physiological and Behavioral data, combined with standardized psychological testing and deep learning algorithms, automatically analyze and generate evaluation reports.
By reducing artificial errors, improving the accuracy of mental illness diagnosis, providing personalized treatment plans, timely discovering mental health problems, preventing the development or worsening of the disease, and improving treatment effect and user satisfaction.
Smart Images
Figure CN119400404A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of medical health technology, and specifically relates to a system and method for psychological assessment of mental illness. Background Art
[0002] Mental illness, also called mental disorders or psychological illness, is a group of conditions that affect thinking, feelings, and behavior. They can be mild, such as occasional anxiety or mood swings, or severe, such as schizophrenia or major depression. Mental illness can be caused by a variety of factors, including genetics, brain chemistry, personality, environment, and life events.
[0003] Psychological testing is a relatively advanced testing method. It refers to a scientific measurement method that uses a series of means to quantify certain psychological characteristics of people to measure the level of users' psychological factors and the differences in users' psychological differences. Psychological testing (test) refers to the use of scientifically designed scales to measure unobservable personality structures. It is also called latent variable testing. In layman's terms, psychological testing refers to a scientific method that uses a series of scientific methods to measure the intelligence level and personality differences of the person being evaluated. Psychological testing (test) is generally conducted in the form of questionnaires designed to meet the reliability and validity requirements. A useful psychological test must be effective.
[0004] Psychological assessment of mental illness is an important step in diagnosing mental disorders. Through assessment, doctors can determine whether the user suffers from a certain mental illness, as well as the type and severity of the illness. Existing psychological assessments of mental illness rely heavily on the doctor's subjective judgment and experience, which may lead to inconsistency and deviation in the assessment results. The diagnosis of mental disorders is based on symptomology rather than etiology, and lacks clinical laboratory physiological markers, resulting in relatively low reliability and validity of the diagnosis. Since the symptoms of mental illness may be similar to other diseases, there is a high risk of misdiagnosis, especially for the distinction between bipolar disorder and depression. Many assessment tools may not have a solid theoretical basis, nor do they have strict compilation and testing procedures, which affects the accuracy of the assessment. Summary of the invention
[0005] The purpose of the present invention is to provide a system and method for psychological assessment of mental illness, so as to solve the problem that psychological assessment of mental illness in the prior art largely relies on the subjective judgment and experience of doctors, resulting in inconsistency and deviation of assessment results.
[0006] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a psychological assessment system for mental illness, which is used to assist in the diagnosis of mental disorders in people with mental disorders, and includes a data collection module, a psychological test module, an artificial intelligence analysis engine, an assessment report generator, a user interaction interface, a cloud database and an expert knowledge base. The data collection module collects the user's physiological data and behavioral data through a wearable device or a mobile application, and the psychological test module integrates a variety of standardized psychological tests and automatically collects test results. The artificial intelligence analysis engine adopts machine learning and deep learning algorithms. The deep learning algorithm combines the user's physiological data, behavioral data and psychological test results to automatically analyze the user's psychological state. The assessment report generator automatically generates a comprehensive assessment report based on the analysis results of the artificial intelligence analysis engine. The user interaction interface provides a friendly operation interface for doctors and users, and the user interaction interface consists of a login system, a data management module, an interactive assessment module, a result display module and a personalized suggestion module. The cloud database is used to store the user's assessment data and historical records, and the cloud database supports remote access and data analysis. The expert knowledge base integrates the diagnostic experience and treatment suggestions of mental health experts to assist the artificial intelligence analysis engine in providing more accurate assessments.
[0007] Preferably, the wearable device includes a smart watch or a health monitoring bracelet, and the mobile application includes a data integration unit and an intervention assistance unit. Smart watches usually run operating systems such as Android Wear OS or Apple's Watch OS. These systems support advanced health monitoring applications, provide user-friendly interfaces and rich functions, and have a variety of sensors, such as heart rate monitors, accelerometers, GPS, etc., which can continuously track and record users' physiological data and daily activities; bracelet devices are usually simpler and focus on the collection of health data, such as steps, calorie consumption, sleep quality, etc. They usually use a real-time operating system (RTOS), which is sufficient to handle these basic tasks. Although the functions are relatively simple, the bracelet can be connected to a smartphone via Bluetooth and display more detailed data and trend analysis through a dedicated application; mobile health applications can integrate physiological data from different devices and provide a unified platform to view and manage health information. These applications may also include personalized health advice and interventions to help users improve their living habits and manage long-term conditions; such devices are designed to monitor multiple physiological parameters simultaneously, such as blood pressure, blood sugar levels, electrocardiograms, etc. They are suitable for users who need close medical monitoring, such as those with heart disease or diabetes, and the devices can usually provide real-time data feedback to users and medical providers to facilitate timely adjustments to treatment plans.
[0008] Preferably, the multiple standardized psychological tests include the Self-Rating Anxiety Scale, the Self-Rating Depression Scale, the Minnesota Multiphasic Personality Questionnaire, the Beck Depression Inventory, the Hamilton Anxiety Rating Scale, the Five-Factor Personality Inventory, the Wechsler Intelligence Scale, and the Raven's Standard Reasoning Test. Through the scale, the user can understand the severity of his or her anxiety and depression symptoms, so as to better understand his or her mental state. The doctor or psychologist can more accurately determine whether the user suffers from a mental disorder based on the results of the scale and the user's clinical manifestations. During the treatment process, the scale can be used to monitor changes in the user's symptoms to evaluate the treatment effect and adjust the treatment plan. For some high-risk groups, such as students, people with high work pressure, etc., regular measurements of the Self-Rating Anxiety and Depression Scales are helpful to detect problems in a timely manner and intervene.
[0009] Preferably, the comprehensive assessment report consists of a mental illness risk assessment, a mental health status analysis, and treatment recommendations. By evaluating the user's life history, family history, and current mental state, various risk factors that may lead to mental illness can be identified. These risk factors may include genetic predispositions, early life experiences, chronic diseases, or drug abuse, etc. By analyzing the user's mental health status, medical professionals can obtain detailed information about their emotions, thinking, and behavior patterns. Based on the results of risk assessment and mental health status analysis, medical professionals can provide users with personalized treatment recommendations, including drug therapy and psychotherapy.
[0010] Preferably, the result display module includes a data visualization unit, which is convenient for doctors and users to observe and analyze data from different angles.
[0011] The psychological assessment method for mental illness includes the following steps:
[0012] Sp1: Users authorize the system through mobile applications or wearable devices and enter personal information. Wearable devices can continuously track and record users' physiological and behavioral data, connect to smartphones via Bluetooth, and view and manage health information through mobile health applications on smartphones, display more detailed data and trend analysis, and upload users' physiological and behavioral data to the artificial intelligence analysis engine;
[0013] Sp2: After the user logs in to the psychological testing platform and inputs personal information, the psychological testing module analyzes the user's cultural background and living environment based on the user information, matches the user with the corresponding type of standardized psychological test, and then completes the standardized psychological test online through the psychological testing module. The system automatically collects the test results and uploads them to the artificial intelligence analysis engine for analysis;
[0014] Sp3: The AI analysis engine organizes the collected physiological data, behavioral data, and psychological test results, ensures the quality and integrity of the data, performs necessary data cleaning and preprocessing, uses signal processing and feature extraction techniques to extract useful information from the raw data, uses historical data to train the selected model, adjusts model parameters, performs cross-validation, optimizes model performance, applies the trained model to new data sets for analysis, automatically executes the algorithm, and integrates the diagnostic experience and treatment recommendations of mental health experts in combination with the expert knowledge base to assist the AI analysis engine in analyzing the user's mental state;
[0015] Sp4: The system generates an assessment report including mental illness risk assessment, mental health status analysis and treatment recommendations, and synchronizes it to the cloud database;
[0016] Sp5: Doctors and users log in user information through the user interface to view the assessment report, and analyze the user's psychological trends based on the result display module. Finally, the personalized suggestion module provides stress management skills and relaxation exercises based on the assessment results.
[0017] Preferably, the deep learning algorithm includes a support vector machine, a neural network or a deep belief network. The support vector machine performs well in processing small sample data sets. Through the kernel function technology, it can process high-dimensional data well, making it linearly separable in the feature space, and can effectively perform pattern recognition and classification. The neural network has a strong nonlinear mapping ability and can approximate any complex nonlinear relationship, which makes it very effective in processing complex pattern recognition and prediction problems. Through learning rules, it automatically learns features and laws from data and has strong self-learning ability and adaptability. The distributed processing method of the neural network gives it strong fault tolerance. Even if some neurons are damaged, the overall performance of the network can still be maintained. As a type of deep learning, the deep belief network is particularly good at processing unstructured complex data such as images and sounds, and can automatically extract high-level features. Through multi-layer pre-training and fine-tuning processes, it can effectively learn the deep features and representations of the data, which is crucial to improving the accuracy of classification and prediction. It can process large-scale data sets and effectively utilize computing resources through layer-by-layer training to improve training efficiency.
[0018] The beneficial effects of the present invention are as follows:
[0019] The present invention adopts a complete psychological assessment system for mental illness. Through standardized assessment tools and procedures, human errors are reduced and the accuracy of mental illness diagnosis is improved. Based on detailed assessment results, personalized treatment plans are formulated for users to improve treatment effects, mental health problems are discovered in a timely manner, and a basis for early intervention is provided, which may prevent the development or deterioration of the disease. Through regular assessments, changes in user conditions are monitored and treatment plans are adjusted in a timely manner. The collected data is used for analysis to provide data support for clinical decision-making. The systematically collected data can be used for scientific research and promote the development of new treatment methods. The application of automation and algorithms improves the efficiency of the assessment process and reduces waiting time. The user-friendly interface and interactive design improve user participation and satisfaction. Remote access and assessment are supported so that users living in remote areas or with limited mobility can also obtain professional assessments. The knowledge of experts in different disciplines is integrated to provide more comprehensive assessment and treatment recommendations. As an educational tool, it helps medical professionals and users understand mental illness and treatment methods, ensures the security and privacy of user data, and complies with legal and regulatory requirements. Through scientific and objective assessment methods, social stigma against mental illness is reduced, and public awareness and understanding of mental health issues are improved. Long-term tracking and management are provided for users to help them better manage their mental health, and the safety of overall medical services is improved by reducing human errors. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 is a system flow chart of the present invention;
[0021] Figure 2 It is a system flow diagram of the user interface of the present invention. DETAILED DESCRIPTION
[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0023] Example:
[0024] like Figure 1 to Figure 2As shown, an embodiment of the present invention provides a mental illness psychological assessment system, which is used to assist in the diagnosis of mental disorders in people with mental disorders, including a data collection module, a psychological test module, an artificial intelligence analysis engine, an assessment report generator, a user interaction interface, a cloud database, and an expert knowledge base. The data collection module collects the user's physiological data and behavioral data through a wearable device or a mobile application. The wearable device includes a smart watch or a health monitoring bracelet, and the mobile application includes a data integration unit and an intervention assistance unit. Smart watches usually run operating systems such as Android Wear OS or Apple's Watch OS. These systems support advanced health monitoring applications, provide user-friendly interfaces and rich functions, and have a variety of sensors, such as heart rate monitors, accelerometers, GPS, etc., which can continuously track and record the user's physiological data and daily activities; bracelet devices are usually simpler and mainly focus on the collection of health data, such as steps, calorie consumption, sleep quality, etc. They usually use a real-time operating system (RTOS), which is sufficient to handle these basic tasks. Although the functions are relatively simple, the bracelet can be connected to a smartphone via Bluetooth and display more detailed data and trend analysis through a dedicated application; mobile health applications can integrate physiological data from different devices and provide a unified platform to view and manage health information. These applications may also include personalized health advice and interventions to help users improve their lifestyles and manage long-term conditions; such devices are designed to monitor multiple physiological parameters at the same time, such as blood pressure, blood sugar levels, electrocardiograms, etc. They are suitable for users who need close medical monitoring, such as heart disease users or diabetes users. The device can usually provide real-time data feedback to users and medical providers to facilitate timely adjustment of treatment plans. Users enter personal information through mobile applications or wearable device authorization systems. Wearable devices can continuously track and record users' physiological and behavioral data, connect to smartphones via Bluetooth, and view and manage health information through mobile health applications on smartphones, display more detailed data and trend analysis, and upload users' physiological and behavioral data to artificial intelligence analysis engines.
[0025] After the user logs in to the psychological testing platform, he / she enters personal information. The psychological testing module analyzes the user's cultural background and living environment based on the user information, matches the corresponding type of standardized psychological test for the user, and then completes the standardized psychological test online through the psychological testing module. The system automatically collects the test results and uploads them to the artificial intelligence analysis engine for analysis; the psychological testing module integrates a variety of standardized psychological tests and automatically collects the test results. The various standardized psychological tests include the Anxiety Self-Rating Scale, the Depression Self-Rating Scale, the Minnesota Multiphasic Personality Questionnaire, the Beck Depression Inventory, the Hamilton Anxiety Rating Scale, the Five-Factor Personality Inventory, the Wechsler Intelligence Scale and the Raven's Standard Reasoning Test. Through the scale, the user can understand the severity of his / her anxiety and depression symptoms, so as to better understand his / her mental state. The doctor or psychologist can more accurately judge whether the user has a mental disorder based on the results of the scale and the user's clinical manifestations. During the treatment process, the scale can be used to monitor the changes in the user's symptoms to evaluate the treatment effect and adjust the treatment plan. For some high-risk groups, such as students and people with high work pressure, regular measurement of the anxiety and depression self-rating scales will help to detect problems in time and intervene.
[0026] The AI analysis engine organizes the collected physiological data, behavioral data, and psychological test results to ensure the quality and integrity of the data, performs necessary data cleaning and preprocessing, uses signal processing and feature extraction techniques to extract useful information from the raw data, uses historical data to train the selected model, adjusts model parameters, performs cross-validation, optimizes model performance, applies the trained model to new data sets for analysis, automatically executes the algorithm, and integrates the diagnostic experience and treatment recommendations of mental health experts in combination with the expert knowledge base to assist the AI analysis engine in analyzing the user's mental state; the AI analysis engine uses machine learning and deep learning algorithms. The deep learning algorithm combines the user's physiological data, behavioral data, and psychological test results to automatically analyze The user's mental state, the assessment report generator automatically generates a comprehensive assessment report based on the analysis results of the artificial intelligence analysis engine. The comprehensive assessment report consists of mental illness risk assessment, mental health status analysis and treatment recommendations. By evaluating the user's life history, family history and current mental state, various risk factors that may lead to mental illness can be identified. These risk factors may include genetic tendencies, early life experiences, chronic diseases or drug abuse, etc. By analyzing the user's mental health status, medical professionals can obtain detailed information about their emotions, thinking and behavior patterns. Based on the results of risk assessment and mental health status analysis, medical professionals can provide users with personalized treatment recommendations, including drug therapy and psychotherapy.
[0027] The user interaction interface provides a friendly operation interface for doctors and users, and the user interaction interface consists of a login system, a data management module, an interactive evaluation module, a result display module and a personalized suggestion module. The cloud database is used to store the user's evaluation data and historical records, and the cloud database supports remote access and data analysis. The expert knowledge base integrates the diagnostic experience and treatment suggestions of mental health experts, and assists the artificial intelligence analysis engine to provide more accurate evaluations. The result display module includes a data visualization unit, which is convenient for doctors and users to observe and analyze data from different angles. Doctors and users log in to user information through the user interface to view the evaluation report, and analyze the user's psychological trends based on the result display module. Finally, the personalized suggestion module provides stress management skills and relaxation exercises based on the evaluation results.
[0028] Deep learning algorithms include support vector machines, neural networks or deep belief networks. Support vector machines perform well when processing small sample data sets. Through kernel function technology, they can process high-dimensional data well, making it linearly separable in the feature space, and can effectively perform pattern recognition and classification. Neural networks have strong nonlinear mapping capabilities and can approximate arbitrarily complex nonlinear relationships, which makes them very effective in dealing with complex pattern recognition and prediction problems. By learning rules, they automatically learn features and laws from data and have strong self-learning ability and adaptability. The distributed processing method of neural networks gives them strong fault tolerance. Even if some neurons are damaged, the overall performance of the network can still be maintained. As a type of deep learning, deep belief networks are particularly good at processing unstructured complex data such as images and sounds, and can automatically extract advanced features. Through multi-layer pre-training and fine-tuning processes, they can effectively learn the deep features and representations of data, which is crucial to improving the accuracy of classification and prediction. They can process large-scale data sets and effectively utilize computing resources through layer-by-layer training to improve training efficiency.
[0029] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.
[0030] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. A mental illness psychological assessment system, which is used to assist in diagnosing mental disorders in people with mental disorders, and is characterized by: It includes a data collection module, a psychological test module, an artificial intelligence analysis engine, an evaluation report generator, a user interaction interface, a cloud database and an expert knowledge base. The data collection module collects the user's physiological data and behavioral data through wearable devices or mobile applications. The psychological test module integrates multiple standardized psychological tests and automatically collects test results. The artificial intelligence analysis engine adopts machine learning and deep learning algorithms. The deep learning algorithm combines the user's physiological data, behavioral data and psychological test results to automatically analyze the user's psychological state. The evaluation report generator automatically generates a comprehensive evaluation report according to the analysis results of the artificial intelligence analysis engine. The user interaction interface provides a friendly operation interface for doctors and users, and the user interaction interface consists of a login system, a data management module, an interactive evaluation module, a result display module and a personalized suggestion module. The cloud database is used to store the user's evaluation data and historical records, and the cloud database supports remote access and data analysis. The expert knowledge base integrates the diagnostic experience and treatment suggestions of mental health experts to assist the artificial intelligence analysis engine in providing more accurate evaluations.
2. The mental illness psychological assessment system according to claim 1, characterized in that: The wearable device includes a smart watch or a health monitoring bracelet, and the mobile application includes a data integration unit and an intervention assistance unit.
3. The mental illness psychological assessment system according to claim 1, characterized in that: The multiple standardized psychological tests include the Self-Rating Anxiety Scale, the Self-Rating Depression Scale, the Minnesota Multiphasic Personality Inventory, the Beck Depression Inventory, the Hamilton Anxiety Rating Scale, the Five-Factor Personality Inventory, the Wechsler Intelligence Scale, and the Raven's Standard Reasoning Test.
4. The mental illness psychological assessment system according to claim 1, characterized in that: The comprehensive assessment report consists of mental illness risk assessment, mental health status analysis and treatment recommendations.
5. The mental illness psychological assessment system according to claim 1, characterized in that: The result display module includes a data visualization unit.
6. A method for psychological assessment of mental illness, characterized in that: The following steps are involved: Sp1: Users authorize the system through mobile applications or wearable devices and enter personal information. Wearable devices can continuously track and record users' physiological and behavioral data, connect to smartphones via Bluetooth, and view and manage health information through mobile health applications on smartphones, display more detailed data and trend analysis, and upload users' physiological and behavioral data to the artificial intelligence analysis engine; Sp2: After the user logs in to the psychological testing platform and inputs personal information, the psychological testing module analyzes the user's cultural background and living environment based on the user information, matches the user with the corresponding type of standardized psychological test, and then completes the standardized psychological test online through the psychological testing module. The system automatically collects the test results and uploads them to the artificial intelligence analysis engine for analysis; Sp3: The AI analysis engine organizes the collected physiological data, behavioral data, and psychological test results, ensures the quality and integrity of the data, performs necessary data cleaning and preprocessing, selects appropriate machine learning or deep learning algorithms according to the analysis purpose, uses signal processing and feature extraction techniques to extract useful information from the raw data, trains the selected model using historical data, adjusts model parameters, performs cross-validation, optimizes model performance, applies the trained model to new data sets for analysis, automatically executes the algorithm, and integrates the diagnostic experience and treatment recommendations of mental health experts in combination with the expert knowledge base to assist the AI analysis engine in analyzing the user's mental state; Sp4: The system generates an assessment report including mental illness risk assessment, mental health status analysis and treatment recommendations, and synchronizes it to the cloud database; Sp5: Doctors and users log in user information through the user interface to view the assessment report, and analyze the user's psychological trends based on the result display module. Finally, the personalized suggestion module provides stress management skills and relaxation exercises based on the assessment results.
7. The method for psychological assessment of mental illness according to claim 6, characterized in that: The deep learning algorithm includes a support vector machine, a neural network or a deep belief network.