Artificial intelligence-based psychosomatic medicine system

Abstract

The invention relates to the technical field of medical information systems, in particular to an artificial intelligence-based psychosomatic medical system, which comprises a network layer; a data acquisition module; the data processing module is connected with the data acquisition module and is used for processing the collected data; the intelligent analysis module is connected with the data processing module and used for analyzing the processed data; according to the system architecture design of the invention, the characteristics of psychosomatic medicine are fully considered, and all the modules are in close cooperation to form a complete diagnosis and treatment closed loop. From data acquisition, intelligent analysis and application output, each link reflects the thought of syndrome differentiation and treatment in psychosomatic medicine. Particularly, in the intelligent analysis module, psychosomatic medical knowledge is innovatively combined with a modern machine learning algorithm, a unique knowledge graph is constructed, psychosomatic medical diagnosis and modern medical standards can be considered at the same time, and a more comprehensive and accurate diagnosis result can be given.

Description

Technical Field

[0001] The present invention relates to the technical field of medical information systems, and in particular to an artificial intelligence-based psychosomatic medicine system. Background Art

[0002] In recent years, with the rapid development of artificial intelligence technology, its application in the medical field has become increasingly extensive. Especially in the integration of traditional medicine and modern technology, artificial intelligence has provided a new opportunity for the modernization of traditional medicine such as Chinese medicine and Mongolian medicine. As an important branch of modern medicine, psychosomatic medicine mainly explores the role of the relationship between psychosocial factors and physical diseases in the prevention, occurrence, development and rehabilitation of diseases. It emphasizes the concept of health of the unity of body and mind and has unique advantages in the treatment of chronic diseases and mental illnesses. However, how to organically combine the theory of psychosomatic medicine with modern medical technology has always been a major challenge facing this field.

[0003] Most existing medical information systems focus on the diagnosis and treatment process, which makes it difficult to fully reflect the characteristics of psychosomatic medicine. Although some systems try to introduce some third-party diagnostic modules, they often simply digitize traditional diagnostic methods and lack deep integration with modern medical theories. This makes it difficult for the system to give comprehensive and accurate diagnostic results in practical applications, and it is also impossible to give full play to the advantages of psychosomatic medicine in holistic treatment.

[0004] On the other hand, existing AI medical systems also have limitations in data processing and analysis. Most systems focus only on the patient's physiological indicators, ignoring the impact of psychological and social factors on health. This single-dimensional data analysis is difficult to meet the comprehensive and holistic diagnosis and treatment needs of psychosomatic medicine. At the same time, the existing system has limited learning capabilities and it is difficult to continuously optimize the diagnostic model based on new diagnosis and treatment experience and feedback, which seriously restricts the continuous improvement of system performance.

[0005] In addition, in terms of formulating personalized treatment plans, the existing system often adopts fixed treatment guidelines and lacks in-depth consideration of individual differences of patients. This is obviously different from the individualized and holistic treatment concept emphasized by psychosomatic medicine. At the same time, the existing system is also insufficient in providing telemedicine services, making it difficult to meet the needs of patients in remote areas for high-quality medical services.

[0006] In view of the above problems, there is an urgent need for a medical system that can truly integrate psychosomatic medicine theory and modern artificial intelligence technology. This system should be able to comprehensively collect and analyze the multi-dimensional health data of patients, realize the organic combination of psychosomatic medicine and modern medical diagnostic standards, provide personalized treatment plans, and have the ability to continuously learn and optimize. At the same time, the system should also be able to provide convenient telemedicine services and expand the coverage of high-quality medical resources. Summary of the invention

[0007] The present invention aims at the above technical problems and proposes a psychosomatic medicine system based on artificial intelligence. Through innovative architecture design and advanced artificial intelligence algorithms, the system realizes the deep integration of psychosomatic medicine theory and modern medical technology, providing patients with comprehensive, accurate and personalized medical services.

[0008] The present invention proposes an artificial intelligence-based psychosomatic medicine system, comprising:

[0009] The network layer is used to connect the public service network and the hospital's internal LAN;

[0010] a data collection module, connected to the network layer, for collecting patient data from multiple sources;

[0011] A data processing module, connected to the data acquisition module, for processing the collected data;

[0012] an intelligent analysis module, connected to the data processing module, for analyzing the processed data; and

[0013] The application module is connected to the intelligent analysis module and is used to provide medical services and system management functions.

[0014] Preferably, the data acquisition module includes:

[0015] A sensor acquisition unit, used to collect physiological data of the patient;

[0016] Medical equipment collection unit, used to collect medical examination data;

[0017] Data interface acquisition unit, used to collect external system data through API interface;

[0018] A questionnaire collection unit, used to collect psychological assessment data from patients; and

[0019] The data aggregation unit is used to initially integrate the collected multi-source data.

[0020] Preferably, the data processing module includes:

[0021] Data cleaning unit, used to remove noise and outliers from raw data;

[0022] Data standardization unit, used to unify data formats and units;

[0023] A data sorting unit is used to perform structured processing on the cleaned data;

[0024] A data classification unit, used for classifying and storing data according to data characteristics; and

[0025] A data storage unit is used to store the processed data in a database management system DBMS. Preferably, the intelligent analysis module includes:

[0026] Machine learning unit, used to implement basic data analysis and prediction;

[0027] Deep learning unit for processing complex nonlinear data relationships;

[0028] Integrated learning unit, used to synthesize the analysis results of multiple models;

[0029] Neural network algorithm unit, used to simulate human brain neurons to process information;

[0030] A clustering algorithm unit for discovering latent groups in the data; and

[0031] A knowledge graph construction unit is used to integrate psychosomatic medicine theory knowledge and modern medical knowledge. Preferably, the application module includes:

[0032] Business application submodule to support patient diagnosis and treatment, medical record management and telemedicine;

[0033] The patient health management submodule is used to provide health monitoring, assessment and guidance services; and the system operation and maintenance management submodule is used to ensure the safe operation and performance optimization of the system.

[0034] Preferably, the business application submodule further includes:

[0035] Intelligent diagnosis unit, used for intelligent diagnosis based on ICD disease diagnosis classification;

[0036] A personalized treatment plan generation unit is used to formulate treatment plans based on the individual characteristics of patients; and a telemedicine unit is used to provide online consultation and remote consultation services.

[0037] Preferably, the patient health management submodule further includes:

[0038] Health monitoring unit, used to collect and analyze patient health data in real time;

[0039] Health Assessment Unit, which is used to regularly assess the patient's overall health; and

[0040] Health coaching unit for providing personalized health advice and lifestyle guidance.

[0041] Preferably, the system operation and maintenance management submodule further includes:

[0042] System monitoring unit, used to monitor the system operation status in real time;

[0043] Security maintenance unit, used to implement data encryption and access control;

[0044] User rights management unit, used to manage system access rights for different roles; and

[0045] Data backup and recovery unit, used to back up data regularly and restore it when needed.

[0046] Preferably, a dynamic learning module is further included, and the dynamic learning module is connected to the intelligent analysis module and is used to:

[0047] Continuously collect new diagnosis and treatment data and feedback information;

[0048] Update the algorithm model in the intelligent analysis module according to the newly collected data; and

[0049] Optimize diagnostic accuracy and effectiveness of treatment options.

[0050] Preferably, it also includes a multimodal data fusion module, which is connected to the data processing module and the intelligent analysis module and is used to:

[0051] Integrate multi-dimensional data of patients, including physiological, psychological and social;

[0052] Build a comprehensive health profile of the patient; and

[0053] Provide comprehensive data input for intelligent analysis modules.

[0054] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0055] The system architecture design of the present invention fully considers the characteristics of psychosomatic medicine, and the various modules work closely together to form a complete closed loop of diagnosis and treatment. From data collection to intelligent analysis, and then to application output, each link embodies the idea of ​​psychosomatic medicine's syndrome differentiation and treatment. Especially in the intelligent analysis module, the present invention innovatively combines the theoretical knowledge of psychosomatic medicine with modern machine learning algorithms to construct a unique knowledge graph that can take into account both psychosomatic medicine diagnosis and modern medical standards, and give more comprehensive and accurate diagnostic results.

[0056] In terms of data processing, the multimodal data fusion module of the present invention effectively solves the limitations of single-dimensional analysis of existing systems. By integrating multi-dimensional data such as physiological, psychological, and social data of patients, the system can construct a comprehensive health portrait of patients, providing a solid foundation for accurate diagnosis and personalized treatment. This comprehensive data analysis not only improves the accuracy of diagnosis, but also better reflects the advantages of holistic treatment of psychosomatic medicine.

[0057] The dynamic learning module of the present invention effectively solves the problem of limited learning ability of existing systems. By continuously collecting new diagnosis and treatment data and feedback information, the system can continuously optimize its diagnostic model and treatment plan to achieve continuous performance improvement. This self-learning and optimization ability enables the system to quickly adapt to medical development and disease trends and maintain long-term effectiveness.

[0058] In terms of formulating personalized treatment plans, the system of the present invention fully considers the individual differences of patients, including not only physiological indicators, but also psychological states and living environments. This comprehensive personalized consideration enables the system to provide more accurate and effective treatment plans, truly realizing the individualized and holistic treatment concept of psychosomatic medicine.

[0059] In addition, the telemedicine function of the present invention effectively expands the coverage of high-quality medical resources. Through online consultation and remote consultation services, patients in remote areas can also enjoy high-quality psychosomatic medical services, which greatly improves the utilization efficiency of medical resources and promotes medical equity.

[0060] In summary, the artificial intelligence-based psychosomatic medicine system of the present invention effectively solves the problems existing in the existing system in terms of psychosomatic medicine theory integration, data analysis, personalized treatment and remote services through innovative design and advanced technology. This system not only improves the accuracy and efficiency of psychosomatic medicine diagnosis and treatment, but also provides strong support for the modernization and promotion of psychosomatic medicine, and has important theoretical significance and practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is the overall architecture diagram of the present invention.

[0062] Figure 2 It is the overall logic block diagram of the system of the present invention.

[0063] Figure 3 It is a logic block diagram of the data acquisition module of the present invention.

[0064] Figure 4 It is a logic block diagram of the data processing module of the present invention.

[0065] Figure 5 It is a logic block diagram of the intelligent analysis module of the present invention.

[0066] Figure 6 It is a logic block diagram of the application module of the present invention.

[0067] Figure 7 It is a logic block diagram of the business application submodule of the present invention.

[0068] Figure 8This is a logical block diagram of the patient health management submodule of the present invention.

[0069] Fig. 9 It is a logical block diagram of the system operation and maintenance management submodule of the present invention.

[0070] Fig.10 It is a logic block diagram of the dynamic learning module of the present invention.

[0071] Fig.11 It is a logic block diagram of the multimodal data fusion module of the present invention. DETAILED DESCRIPTION

[0072] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects thereof are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0073] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0074] Example 1

[0075] First, if Figure 1-11 As shown, the present invention provides a psychosomatic medicine system based on artificial intelligence, including a network layer 1, a data acquisition module 2, a data processing module 3, an intelligent analysis module 4 and an application module 5. These modules are closely connected through information flow and workflow to form a complete system architecture.

[0076] Specifically, network layer 1 is used to connect the public service network and the hospital's internal LAN. This dual network structure ensures the openness of the system while taking into account the security of medical data. For example, patients can access health consultation services through the public service network, while doctors can securely access patients' detailed medical records in the hospital's internal LAN.

[0077] The data acquisition module 2 is connected to the network layer 1 and is used to collect patient data from multiple sources. In one embodiment of the present invention, the data acquisition module 2 includes a sensor acquisition unit 21, a medical device acquisition unit 22, a data interface acquisition unit 23, a questionnaire acquisition unit 24 and a data aggregation unit 25. This multi-source data acquisition method ensures the comprehensiveness and accuracy of the data.

[0078] Preferably, the sensor acquisition unit 21 can collect physiological data such as the patient's heart rate, blood pressure, and body temperature. The medical equipment acquisition unit 22 can collect medical imaging data such as CT and MRI. The data interface acquisition unit 23 connects with other medical systems through the API interface to obtain information such as the patient's medical history. The questionnaire collection unit 24 is used to collect subjective data such as the patient's psychological state and living habits. Finally, the data aggregation unit 25 preliminarily integrates the data from these different sources in preparation for subsequent processing.

[0079] Next, the data processing module 3 is connected to the data acquisition module 2 to process the collected data. In one embodiment of the present invention, the data processing module 3 includes a data cleaning unit 31, a data standardization unit 32, a data sorting unit 33, a data classification unit 34 and a data storage unit 35.

[0080] Specifically, the data cleaning unit 31 will first remove the noise and abnormal values ​​in the original data. For example, for heart rate data, if there are obviously abnormal values ​​(such as a heart rate of 0 or more than 300), the system will automatically identify and remove these data. Then, the data standardization unit 32 will unify the format and unit of data from different sources. For example, all temperature data are uniformly converted to degrees Celsius. Next, the data sorting unit 33 performs structured processing on the cleaned data to facilitate subsequent analysis. The data classification unit 34 classifies and stores the data according to its characteristics, such as storing physiological data, psychological data, medical history data, etc. separately. Finally, the data storage unit 35 stores the processed data in a database management system (DBMS) to ensure the security and accessibility of the data.

[0081] The intelligent analysis module 4 is connected to the data processing module 3 and is the core part of the system. It includes a machine learning unit 41, a deep learning unit 42, an integrated learning unit 43, a neural network algorithm unit 44, a clustering algorithm unit 45 and a knowledge graph construction unit 46. These units work together to realize intelligent analysis of patient data.

[0082] For example, the machine learning unit 41 can use the support vector machine (SVM) algorithm to classify the patient's physiological indicators and preliminarily determine whether the patient has a certain disease risk. The deep learning unit 42 can use the convolutional neural network (CNN) to process medical imaging data to assist doctors in imaging diagnosis. The integrated learning unit 43 improves the accuracy of diagnosis by combining the results of multiple models. The neural network algorithm unit 44 can simulate the way human brain neurons process information and model complex nonlinear relationships. The clustering algorithm unit 45 can use algorithms such as K-means to discover potential groups in patient data, which is helpful for identifying disease subtypes.

[0083] Particularly worth mentioning is the knowledge graph construction unit 46, which integrates TCM theoretical knowledge and modern medical knowledge to form a unique knowledge base. This knowledge graph not only contains the theory of traditional Chinese medicine, but also combines the classification and understanding of diseases in modern medicine. For example, when processing the data of a patient with headache, the system will consider both the theory of "wind evil attacking the head" in TCM and the diagnostic criteria of migraine in modern medicine, so as to give a more comprehensive analysis result.

[0084] The application module 5 is connected to the intelligent analysis module 4 and is the interface for the system to interact with the user. It includes a business application submodule 51, a patient health management submodule 52 and a system operation and maintenance management submodule 53. These three submodules provide comprehensive functional support for the needs of doctors, patients and system administrators.

[0085] The business application submodule 51 further includes an intelligent diagnosis unit 511, a personalized treatment plan generation unit 512, and a telemedicine unit 513. The intelligent diagnosis unit 511 performs intelligent diagnosis based on the ICD disease diagnosis classification, which not only standardizes the diagnosis process, but also realizes the organic integration of traditional Chinese medicine and modern medicine. For example, when a patient has the symptom of "epigastric pain", the system will consider the diagnosis of "stomach heat syndrome" in traditional Chinese medicine and "gastritis" in modern medicine at the same time, and give a comprehensive diagnosis suggestion.

[0086] The personalized treatment plan generation unit 512 formulates a treatment plan based on the individual characteristics of the patient. The "individual characteristics" here include not only the patient's physiological indicators, but also factors such as his or her psychological state and living environment. For example, for a patient diagnosed with "liver depression and qi stagnation", the system will not only recommend basic comfort and effective communication to psychological therapy and psychotropic drug treatment integrated in medical management, but also give conditioning suggestions based on the patient's living habits, such as increasing outdoor activity time.

[0087] The telemedicine unit 513 provides online consultation and remote consultation services, which greatly improves the utilization efficiency of medical resources and enables patients in remote areas to enjoy high-quality medical services. For example, a patient living in a grassland area can communicate with experts in a city hospital in real time through a video link and obtain professional diagnosis and treatment opinions.

[0088] The patient health management submodule 52 includes a health monitoring unit 521, a health assessment unit 522, and a health guidance unit 523. The health monitoring unit 521 can collect and analyze health data in real time through the smart device worn by the patient. The health assessment unit 522 regularly assesses the patient's overall health status, which includes not only physical health, but also mental health and social adaptability. The health guidance unit 523 provides personalized health advice and lifestyle guidance to patients based on these assessment results.

[0089] The system operation and maintenance management submodule 53 ensures the safe operation and performance optimization of the system. It includes a system monitoring unit 531, a security maintenance unit 532, a user authority management unit 533 and a data backup and recovery unit 534. These units work together to ensure the stability, security and reliability of the system.

[0090] The present invention also includes a dynamic learning module 6, which is connected to the intelligent analysis module 4 and is used to continuously optimize the performance of the system. The dynamic learning module 6 continuously collects new diagnosis and treatment data and feedback information, and uses these data to update the algorithm model in the intelligent analysis module 4. For example, if the system finds that a new combination of symptoms is highly correlated with a specific disease, it will automatically update the diagnosis model to improve the accuracy of future diagnoses.

[0091] Finally, the multimodal data fusion module 7 is connected to the data processing module 3 and the intelligent analysis module 4 to integrate the patient's multi-dimensional data such as physiology, psychology and society. This multimodal data fusion enables the system to build a comprehensive health portrait of the patient and provide more comprehensive data input for intelligent analysis. For example, when assessing a patient's risk of depression, the system will not only consider the patient's psychological evaluation results, but also conduct a comprehensive analysis based on their physiological indicators (such as sleep quality) and social factors (such as work pressure).

[0092] Through the close collaboration of the above modules, the artificial intelligence-based psychosomatic medicine system of the present invention realizes the deep integration of psychosomatic medicine theory and modern medical technology, providing patients with comprehensive, accurate and personalized medical services. This innovative system design not only improves the accuracy of diagnosis and the effectiveness of treatment, but also promotes the modernization of psychosomatic medicine and provides a new technical path for the integration of traditional Chinese and Western medicine.

[0093] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A psychosomatic medicine system based on artificial intelligence, characterized in that: include: The network layer is used to connect the public service network and the hospital's internal LAN; a data collection module, connected to the network layer, for collecting patient data from multiple sources; A data processing module, connected to the data acquisition module, for processing the collected data; An intelligent analysis module, connected to the data processing module, for analyzing the processed data; as well as The application module is connected to the intelligent analysis module and is used to provide medical services and system management functions.

2. The system according to claim 1, characterized in that The data acquisition module comprises: A sensor acquisition unit, used to collect physiological data of the patient; Medical equipment collection unit, used to collect medical examination data; Data interface acquisition unit, used to collect external system data through API interface; A questionnaire collection unit, used to collect psychological assessment data from patients; and The data aggregation unit is used to initially integrate the collected multi-source data.

3. The system according to claim 1, characterized in that The data processing module comprises: Data cleaning unit, used to remove noise and outliers from raw data; Data standardization unit, used to unify data formats and units; A data sorting unit is used to perform structured processing on the cleaned data; A data classification unit, used for classifying and storing data according to data characteristics; and The data storage unit is used to store the processed data in the database management system DBMS.

4. The system according to claim 1, characterized in that The intelligent analysis module includes: Machine learning unit, used to implement basic data analysis and prediction; Deep learning unit for processing complex nonlinear data relationships; Integrated learning unit, used to synthesize the analysis results of multiple models; Neural network algorithm unit, used to simulate human brain neurons to process information; A clustering algorithm unit for discovering latent groups in the data; and A knowledge graph construction unit used to integrate Mongolian medicine theoretical knowledge and modern medical knowledge.

5. The system according to claim 1, characterized in that The application module includes: Business application submodule to support patient diagnosis and treatment, medical record management and telemedicine; Patient health management submodule, which provides health monitoring, assessment and guidance services; and The system operation and maintenance management submodule is used to ensure the safe operation and performance optimization of the system.

6. The system according to claim 5, characterized in that The business application submodule further includes: Intelligent diagnosis unit, used for intelligent diagnosis based on ICD disease diagnosis classification; A personalized treatment plan generation unit, used to formulate a treatment plan based on individual patient characteristics; and Telemedicine unit, used to provide online consultation and remote consultation services.

7. The system according to claim 5, characterized in that The patient health management submodule further includes: Health monitoring unit, used to collect and analyze patient health data in real time; Health Assessment Unit, which is used to regularly assess the patient's overall health; and Health coaching unit for providing personalized health advice and lifestyle guidance.

8. The system according to claim 5, characterized in that The system operation and maintenance management submodule further includes: System monitoring unit, used to monitor the system operation status in real time; Security maintenance unit, used to implement data encryption and access control; User rights management unit, used to manage system access rights for different roles; and Data backup and recovery unit, used to back up data regularly and restore it when needed.

9. The system according to claim 1, characterized in that It also includes a dynamic learning module, which is connected to the intelligent analysis module and is used to: Continuously collect new diagnosis and treatment data and feedback information; Update the algorithm model in the intelligent analysis module according to the newly collected data; and Optimize diagnostic accuracy and effectiveness of treatment options.

10. The system according to claim 1, characterized in that It also includes a multimodal data fusion module, which is connected to the data processing module and the intelligent analysis module and is used to: Integrate multi-dimensional data of patients, including physiological, psychological and social; Build a comprehensive health profile of the patient; and Provide comprehensive data input for intelligent analysis modules.

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