Intelligent mental disorder diagnosis and treatment device and method based on big data and medium

Abstract

The invention provides a mental disorder intelligent diagnosis and treatment device and method based on big data and a medium. The device comprises a diagnosis equipment module and a treatment equipment module, the diagnosis equipment module comprises a data acquisition module, a data processing module and an analysis and diagnosis module, and the treatment equipment module comprises a treatment scheme making module, a treatment execution module and a rehabilitation follow-up visit module. The device executes the following method: acquiring comprehensive information data of a target object, respectively processing the comprehensive information data to obtain mental disorder degree parameters, mental disorder patient induction factors and mental disorder latent factors, further processing the mental disorder degree parameters, the mental disorder patient induction factors and the mental disorder latent factors to obtain mental disorder patient risk assessment data, and judging mental disorder symptoms of the target object. A corresponding treatment scheme is formulated according to the symptoms of the mental disorder, so that the intelligent diagnosis and treatment technology of the mental disorder based on the big data is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices and methods, and more particularly to an intelligent diagnosis and treatment device, method and medium for mental disorders based on big data. Background Art

[0002] The diagnosis of mental disorders currently mainly relies on doctors' clinical experience and some subjective psychological assessment scales, which have problems such as insufficient diagnostic accuracy and high misdiagnosis and missed diagnosis rates. For example, the prior art method, device and system for diagnosing and treating mood disorders (CN104244842A) disclose a method and device for diagnosing and / or treating mood disorders such as depression. Compared with subjects without mood disorders, the right vestibular function of subjects diagnosed with mood disorders is weakened. Therefore, a method and device for measuring the right vestibular function of a subject are provided, based on which the diagnosis of mood disorders can be determined. Stimulation of the vestibular system of a subject diagnosed with a mood disorder can reduce the symptoms of the mood disorder of the subject. A method and device for recording the response of a subject to the stimulation of the vestibular system and providing therapeutic vestibular stimulation based on the response of the subject are provided. The response of the subject to the stimulation of the vestibular system can be based on the measurement of the nystagmus of the subject. The prior art method for selecting variables for predicting symptoms of depression, computer device and storage medium (CN113693584A), the disclosed method includes the following steps: investigating depression patients and normal controls, collecting general information, Hamilton Depression Rating Scale scores and resting-state functional magnetic resonance imaging scan data; after the depression patients receive treatment and their individual clinical symptoms are relieved, collecting the above information again; processing the resting-state functional magnetic resonance imaging scan data to obtain a DC brain map; according to the obtained DC brain map, analyzing the changes in brain function activities of depression patients before and after treatment, and finding variables that can characterize the remission of depression symptoms.

[0003] With the development of information technology and artificial intelligence, using multi-source data for the auxiliary diagnosis of mental disorders has become a research hotspot, but the above existing diagnostic methods often fail to fully integrate various data types, resulting in limited diagnostic efficiency. Summary of the Invention

[0004] The object of the present invention is to provide a big data-based intelligent diagnosis and treatment device, method and medium for mental disorders. The device includes a diagnosis device module and a treatment device module. The diagnosis device module includes a data acquisition module, a data processing module and an analysis and diagnosis module. The treatment device module includes a treatment plan formulation module, a treatment execution module and a rehabilitation follow-up module. The device executes the following method: acquiring physiological characteristic data, psychological characteristic data, social characteristic data and medical history characteristic data of a target object, respectively processing them through a preset preliminary mental disorder assessment model and a preset medical history detection model to obtain mental disorder inducing factors and mental disorder latent factors, acquiring comprehensive status data of the target object, processing the data according to a preset mental disorder detection model to obtain a mental disorder patient degree parameter, processing in combination with the mental disorder inducing factors and the mental disorder latent factors to obtain mental disorder patient risk assessment data, and judging the mental disorder symptoms of the target object, and formulating a corresponding treatment plan according to the mental disorder symptoms, so as to realize the technology of big data-based intelligent diagnosis and treatment of mental disorders.

[0005] To solve the above technical problems, the technical solution of the present invention is as follows:

[0006] The present invention also provides a big data-based intelligent diagnosis and treatment device for mental disorders, including:

[0007] A diagnosis device module, including a data acquisition module, a data processing module and an analysis and diagnosis module;

[0008] The data acquisition module is used for data acquisition of a target object;

[0009] The data processing module is used for data integration, feature extraction and data cleaning of the acquired data;

[0010] The analysis and diagnosis module is used for model construction, data comparison and analysis and diagnosis decision-making.

[0011] Optionally, in the big data-based intelligent diagnosis and treatment device of the present invention, it further includes:

[0012] A treatment device module, including a treatment plan formulation module, a treatment execution module and a rehabilitation follow-up module;

[0013] The treatment plan formulation module is used for plan generation and knowledge integration;

[0014] The treatment execution module is used for executing patient treatment, including drug treatment, physical treatment and psychological treatment;

[0015] The rehabilitation follow-up module is used for rehabilitation monitoring, follow-up management and data feedback.

[0016] Second aspect, the present invention also provides a big data-based intelligent diagnosis and treatment method for mental disorders, including the following steps:

[0017] Obtain the physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data of the target object;

[0018] Respectively process the data according to the physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data through a preset preliminary mental disorder assessment model and a preset medical history detection model to obtain mental disorder inducing factors and mental disorder latent factors;

[0019] Obtain the comprehensive status data of the target object, including clinical symptom data and psychological assessment data, and process the data according to a preset mental disorder detection model to obtain a mental disorder patient degree parameter;

[0020] Process according to the mental disorder patient degree parameter in combination with the mental disorder inducing factors and mental disorder latent factors to obtain mental disorder patient risk assessment data, and judge the mental disorder symptoms of the target object;

[0021] Formulate a corresponding treatment plan according to the mental disorder symptoms.

[0022] Optionally, in the big data-based intelligent diagnosis and treatment method for mental disorders of the present invention, the obtaining of the physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data of the target object includes:

[0023] Obtain the personal status information of the target object, and extract personal status characteristic data, including physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data;

[0024] The physiological characteristic data includes neuroimaging data, genetic gene data, and neurotransmitter level data;

[0025] The psychological characteristic data includes personality trait data, cognitive function data, and psychological stress data;

[0026] The social characteristic data includes life event data, social support data, and economic status data;

[0027] The medical history characteristic data includes personal past medical history data and family mental history data.

[0028] Optionally, in the method for intelligent diagnosis and treatment of mental disorders based on big data according to the present invention, the data processing of the physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data through a preset preliminary mental disorder assessment model and a preset medical history detection model to obtain the mental disorder inducing factors and mental disorder latent factors includes:

[0029] Based on the neuroimaging data, genetic gene data, neurotransmitter level data, personality trait data, cognitive function data, and psychological stress data, in combination with the life event data, social support data, and economic status data, data processing is performed through a preset preliminary mental disorder assessment model to obtain the mental disorder inducing factors;

[0030] Based on the personal past medical history data and family mental history data, data processing is performed through a preset medical history detection model to obtain the mental disorder latent factors.

[0031] Optionally, in the method for intelligent diagnosis and treatment of mental disorders based on big data according to the present invention, the obtaining of the comprehensive status data of the target object, including clinical symptom data and psychological assessment data, and data processing according to a preset mental disorder detection model to obtain the mental disorder patient degree parameter includes:

[0032] Obtain the comprehensive status data of the target object, including clinical symptom data and psychological assessment data;

[0033] The clinical symptom data includes symptom manifestation data and symptom duration data;

[0034] The psychological assessment data includes self-rating inventory data and intelligence test data;

[0035] Based on the symptom manifestation data and symptom duration data, in combination with the self-rating inventory data and intelligence test data, data processing is performed through a preset mental disorder detection model to obtain the mental disorder patient degree parameter.

[0036] Optionally, in the method for intelligent diagnosis and treatment of mental disorders based on big data according to the present invention, the processing of the mental disorder patient degree parameter in combination with the mental disorder inducing factors and mental disorder latent factors to obtain the mental disorder patient risk assessment data and judging the mental disorder symptoms of the target object includes:

[0037] Based on the mental disorder patient degree parameter in combination with the mental disorder inducing factors and mental disorder latent factors, processing is performed through a preset mental disorder diagnosis model to obtain the mental disorder patient risk assessment data;

[0038] Compare the mental disorder patient risk assessment data with the preset mental disorder patient risk level threshold to obtain the mental disorder symptom risk level;

[0039] Among them, the preset order risk level evaluation threshold includes the first preset mental disorder patient risk level threshold, the second preset mental disorder patient risk level threshold, and the third preset mental disorder patient risk level threshold, and the first preset mental disorder patient risk level threshold is less than the second preset mental disorder patient risk level threshold, and the second preset mental disorder patient risk level threshold is less than the third preset mental disorder patient risk level threshold;

[0040] If the mental disorder patient risk assessment data is less than or equal to the first preset mental disorder patient risk level threshold, it is determined that the mental disorder symptoms of the target object are normal;

[0041] If the mental disorder patient risk assessment data is greater than the first preset mental disorder patient risk level threshold and less than or equal to the second preset mental disorder patient risk level threshold, it is determined that the mental disorder symptoms of the target object are mild symptoms;

[0042] If the mental disorder patient risk assessment data is greater than the second preset mental disorder patient risk level threshold and less than or equal to the third preset mental disorder patient risk level threshold, it is determined that the mental disorder symptoms of the target object are moderate symptoms;

[0043] If the mental disorder patient risk assessment data is greater than the third preset mental disorder patient risk level threshold, it is determined that the mental disorder symptoms of the target object are severe symptoms.

[0044] Optionally, in the method for intelligent diagnosis and treatment of mental disorders based on big data according to the present invention, the formulating a corresponding treatment plan according to the mental disorder symptoms includes:

[0045] If the mental disorder symptoms of the target object are normal, there is no need to formulate a treatment plan;

[0046] If the mental disorder symptoms of the target object are mild symptoms, a corresponding treatment plan is formulated, and the plan includes psychotherapy and lifestyle adjustment;

[0047] If the mental disorder symptoms of the target object are moderate symptoms, a corresponding treatment plan is formulated, and the plan includes drug treatment and psychotherapy;

[0048] If the mental disorder symptoms of the target object are severe symptoms, a corresponding treatment plan is formulated, and the plan includes hospitalization treatment and rehabilitation treatment.

[0049] Optionally, in the method for intelligent diagnosis and treatment of mental disorders based on big data according to the present invention, it further includes:

[0050] Obtain the performance index data of the preset mental disorder diagnosis model, including sensitivity data, specificity data, and Cohen's Kappa coefficient data;

[0051] Perform weighted processing according to the sensitivity data, specificity data, and Cohen's Kappa coefficient data to obtain a model diagnosis reliability parameter;

[0052] Compare the model diagnosis reliability parameter with a preset model diagnosis reliability threshold to obtain a second threshold comparison result;

[0053] If the second threshold comparison result is less than the preset threshold, the performance reliability of the preset mental disorder diagnosis model does not meet the standard and corresponding adjustments are required.

[0054] In a third aspect, the present invention also provides a computer-readable storage medium, in which a program for the method for intelligent diagnosis and treatment of mental disorders based on big data is stored. When the program for the method for intelligent diagnosis and treatment of mental disorders based on big data is executed by a processor, the steps of the method for intelligent diagnosis and treatment of mental disorders based on big data as described in any one of the above are implemented.

[0055] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0056] The device, method, and medium for intelligent diagnosis and treatment of mental disorders based on big data provided by the present invention include a diagnosis device module and a treatment device module. The diagnosis device module includes a data acquisition module, a data processing module, and an analysis and diagnosis module. The treatment device module includes a treatment plan formulation module, a treatment execution module, and a rehabilitation follow-up module. The device executes the following method: obtain the physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data of a target object, perform data processing on the physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data through a preset preliminary mental disorder assessment model and a preset medical history detection model respectively to obtain mental disorder inducing factors and mental disorder latent factors, obtain the comprehensive status data of the target object, including clinical symptom data and psychological assessment data, perform data processing according to a preset mental disorder detection model to obtain a mental disorder patient degree parameter, process the mental disorder patient degree parameter in combination with the mental disorder inducing factors and mental disorder latent factors to obtain mental disorder patient risk assessment data, and judge the mental disorder symptoms of the target object, and formulate a corresponding treatment plan according to the mental disorder symptoms, so as to realize the technology of intelligent diagnosis and treatment of mental disorders based on big data.

[0057] Other features and advantages of the present invention will be described in the following specification, and in part will be obvious from the specification, or can be understood by implementing the embodiments of the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures specifically pointed out in the written specification and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required to be used in the embodiments of the present invention. It should be understood that the following drawings only show some embodiments of the present invention, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0059] Figure 1 It is a block diagram of the diagnostic device module of the intelligent diagnosis and treatment device for mental disorders based on big data provided by the embodiments of the present invention;

[0060] Figure 2 It is a block diagram of the treatment device module of the intelligent diagnosis and treatment device for mental disorders based on big data provided by the embodiments of the present invention;

[0061] Figure 3 It is a high-level flowchart of the method for intelligent diagnosis and treatment of mental disorders based on big data provided by the embodiments of the present invention, and these methods can be executed and implemented by the device of the present invention;

[0062] Figure 4 It is a flowchart of the method for intelligent diagnosis and treatment of mental disorders based on big data provided by the embodiments of the present invention;

[0063] Figure 5 It is a flowchart of obtaining the mental disorder patient degree parameter of the method for intelligent diagnosis and treatment of mental disorders based on big data provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, rather than all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0065] It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present invention, terms such as "first" and "second" are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0066] Please refer to Figure 1 , Figure 1 which is a block diagram of the diagnostic device module of the intelligent diagnosis and treatment device for mental disorders based on big data in some embodiments of the present invention. According to the embodiments of the present invention, the intelligent diagnosis and treatment device for mental disorders based on big data includes:

[0067] The diagnostic device module includes a data acquisition module, a data processing module, and an analysis and diagnosis module;

[0068] The data acquisition module is used to collect data on the target object;

[0069] The data processing module is used to perform data integration, feature extraction, and data cleaning on the collected data;

[0070] The analysis and diagnosis module is used for model construction, data comparison and analysis, and diagnostic decision-making.

[0071] It should be noted that for the diagnostic device module part, it consists of three major parts: a data acquisition module, a data processing module, and an analysis and diagnosis module. Among them, the data acquisition module is used to collect and obtain the original information and data required for diagnosis or analysis, etc. The data processing module then performs tasks such as data integration, feature extraction, and data cleaning on the collected original information and data to prepare for the next data analysis and index diagnosis. The analysis and diagnosis module constructs a module, then performs comparative analysis and processing on the processed data to obtain the required index parameters and make diagnostic decisions.

[0072] Please refer to Figure 2 , Figure 2 which is a block diagram of the treatment device module of the intelligent diagnosis and treatment device for mental disorders based on big data in some embodiments of the present invention. According to the embodiments of the present invention, the intelligent diagnosis and treatment device for mental disorders based on big data further includes:

[0073] The treatment device module includes a treatment plan formulation module, a treatment execution module, and a rehabilitation follow-up module;

[0074] The treatment plan formulation module is used for plan generation and knowledge integration;

[0075] The treatment execution module is used to execute patient treatment, including drug treatment, physical treatment, and psychological treatment;

[0076] The rehabilitation follow-up module is used for rehabilitation monitoring, follow-up management, and data feedback.

[0077] It should be noted that the treatment device module consists of three major parts: a treatment plan formulation module, a treatment execution module, and a rehabilitation follow-up module. Among them, the treatment plan formulation module is used to formulate corresponding treatment plans for the corresponding diagnosis results. For example, for patients with mild mental disorders, after receiving the diagnosis results, the treatment plan formulation module retrieves the corresponding treatment plan from the preset treatment plan database. This plan is targeted at the target patient and includes necessary psychological treatment and lifestyle adjustments. The treatment execution module is used to execute the corresponding treatment plan for the patient. For example, for the treatment plan of patients with moderate symptoms, this module executes the corresponding drug treatment and psychological treatment, while the rehabilitation follow-up module is used for rehabilitation monitoring, follow-up management, and data feedback.

[0078] Please refer to Figure 3 , Figure 3 which is the high-level flowchart of the intelligent diagnosis and treatment method for mental disorders based on big data provided by the embodiments of the present invention. These methods can be executed and implemented through the device of the present invention.

[0079] It should be noted that the device of the present invention includes a diagnosis device module and a treatment device module. The diagnosis device module includes a data acquisition module, a data processing module, and an analysis and diagnosis module. The treatment device module includes a treatment plan formulation module, a treatment execution module, and a rehabilitation follow-up module. The device executes the following methods: obtaining the comprehensive information data of the target object, respectively processing to obtain the mental disorder patient degree parameter, the mental disorder patient inducing factor, and the mental disorder latent factor, further processing to obtain the mental disorder patient risk assessment data, and judging the mental disorder symptoms of the target object, and formulating corresponding treatment plans according to the mental disorder symptoms. Among them, for the collection, processing, and analysis of data, they are all executed and implemented by the diagnosis device module in the device of the present invention. And for the index parameters obtained after collection, processing, and analysis, the diagnosis can still be executed through this module to obtain the diagnosis results. For the corresponding diagnosis results, the treatment device module correspondingly formulates treatment plans, executes treatment, and conducts rehabilitation follow-up.

[0080] In a second aspect, the present invention also discloses an intelligent diagnosis and treatment method for mental disorders based on big data. Refer to Figure 4 , Figure 4 which is the flowchart of the intelligent diagnosis and treatment method for mental disorders based on big data in some embodiments of the present invention. This intelligent diagnosis and treatment method for mental disorders based on big data is used in terminal devices such as computers and mobile phone terminals. This intelligent diagnosis and treatment method for mental disorders based on big data includes the following steps:

[0081] S41. Obtain the physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data of the target object;

[0082] S42. Respectively perform data processing on the physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data through a preset preliminary mental disorder assessment model and a preset medical history detection model to obtain mental disorder inducing factors and mental disorder latent factors;

[0083] S43. Obtain the comprehensive status data of the target object, including clinical symptom data and psychological assessment data, and perform data processing according to a preset mental disorder detection model to obtain a mental disorder patient degree parameter;

[0084] S44. Process according to the mental disorder patient degree parameter in combination with the mental disorder inducing factors and mental disorder latent factors to obtain mental disorder patient risk assessment data, and judge the mental disorder symptoms of the target object;

[0085] S45. Develop a corresponding treatment plan according to the mental disorder symptoms.

[0086] It should be noted that mental disorders refer to a group of diseases in which, under the influence of various biological, psychological, and social environmental factors, the brain function is disordered, resulting in varying degrees of disorders in mental activities such as cognition, emotion, will, and behavior. Common mental disorders include seasonal depression, major depressive disorder, bipolar depressive episode, dementia with agitation and sleep disorders, and postpartum depression, etc. The diagnosis of mental disorders currently mainly relies on doctors' clinical experience and some subjective psychological assessment scales, and there are problems such as insufficient diagnostic accuracy and a relatively high misdiagnosis and missed diagnosis rate. With the development of information technology and artificial intelligence, the use of multi-source data for the auxiliary diagnosis of mental disorders has become a research hotspot. However, existing diagnostic methods often fail to fully integrate various data types, resulting in limited diagnostic efficacy. Therefore, a reliable and comprehensive big data-based intelligent diagnosis and treatment method for mental disorders is needed. In this embodiment, first, physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data of the target object are obtained. According to the physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data, data processing is respectively performed through a preset preliminary mental disorder assessment model and a preset medical history detection model to obtain mental disorder inducing factors and mental disorder latent factors. The comprehensive status data of the target object is obtained, including clinical symptom data and psychological assessment data. According to the preset mental disorder detection model, data processing is performed to obtain the mental disorder patient degree parameter. According to the mental disorder patient degree parameter, combined with the mental disorder inducing factors and mental disorder latent factors, processing is performed to obtain mental disorder patient risk assessment data, and the mental disorder symptoms of the target object are judged. According to the mental disorder symptoms, a corresponding treatment plan is formulated, thereby realizing the technology of big data-based intelligent diagnosis and treatment of mental disorders.

[0087] According to an embodiment of the present invention, the obtaining of the physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data of the target object includes:

[0088] Obtain the personal status information of the target object and extract personal status characteristic data, including physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data;

[0089] The physiological characteristic data includes neuroimaging data, genetic gene data, and neurotransmitter level data;

[0090] The psychological characteristic data includes personality trait data, cognitive function data, and psychological stress data;

[0091] The social characteristic data includes life event data, social support data, and economic status data;

[0092] The medical history characteristic data includes personal past medical history data and family mental history data.

[0093] It should be noted that in order to more accurately diagnose whether a target patient has a mental disorder, it is necessary to collect some data related to the individual's mental disorder, including physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data. Among them, the physiological characteristic data includes neuroimaging data, genetic data, and neurotransmitter level data. Among them, for neuroimaging data, it is found through techniques such as magnetic resonance imaging (MRI) that schizophrenia patients often have abnormalities in brain structure and function, such as reduced volume of the frontal lobe and temporal lobe, hippocampal atrophy, and disorders in the brain's neural connection network. For genetic data, studies have found that specific gene mutations are related to the susceptibility to mental disorders. For example, the polymorphism of the serotonin transporter gene (5-HTT) is related to depression, and abnormal levels of neurotransmitters such as dopamine, serotonin, and gamma-aminobutyric acid are related to various mental disorders. The psychological characteristic data includes personality trait data, cognitive function data, and psychological stress data. Among them, through means such as personality tests, it can be found that people with neurotic personality traits are more likely to experience negative emotions, have poor emotional stability, are more sensitive to stress, and have a higher risk of suffering from mental disorders such as anxiety and depression. Through cognitive function tests, it can be found that before the onset of mental disorders, individuals may experience a gradual decline in cognitive function, such as inattention, memory loss, and slow thinking. In addition, individuals who are in a high psychological stress state for a long time, such as those experiencing continuous work pressure and tense interpersonal relationships, have an increased risk of developing mental disorders. The social characteristic data includes life event data, social support data, and economic status data. The life event scale can evaluate major life events experienced by an individual, and the social support rating scale can be used to measure the degree of social support an individual has. Economic status data such as income level and poverty level are also related to mental disorders. The medical history characteristic data includes personal past medical history data and family mental history data. Understanding whether the patient has suffered from other physical or mental diseases in the past, as well as the treatment situation, certain physical diseases such as hypothyroidism may cause depressive mood, and for patients with a history of recurrence of mental diseases, the symptom characteristics and treatment responses are also of important reference value for this diagnosis. On the other hand, mental disorders have a certain genetic tendency. Understanding whether there are mental disease patients in the family and the specific disease types can provide a reference for diagnosis.

[0094] According to an embodiment of the present invention, the physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data are respectively processed through a preset mental disorder preliminary evaluation model and a preset medical history detection model to obtain mental disorder inducing factors and mental disorder latent factors, including:

[0095] Based on the neuroimaging data, genetic data, neurotransmitter level data, personality trait data, cognitive function data, and psychological stress data, combined with the life event data, social support data, and economic status data, data processing is performed through a preset preliminary mental disorder assessment model to obtain the inducing factors for mental disorder patients;

[0096] Based on the personal past medical history data and family mental illness history data, data processing is performed through a preset medical history detection model to obtain the latent factors for mental disorder.

[0097] It should be noted that based on the neuroimaging data, genetic data, neurotransmitter level data, personality trait data, cognitive function data, and psychological stress data, combined with the life event data, social support data, and economic status data, data processing is performed through a preset preliminary mental disorder assessment model to obtain the inducing factors for mental disorder patients. This inducing factor can, to a certain extent, reflect the risk of mental disorder occurrence. Among them, the preliminary mental disorder assessment model belongs to a neural network model, which is trained on a large amount of historical neuroimaging data, genetic data, neurotransmitter level data, personality trait data, cognitive function data, psychological stress data, life event data, social support data, and economic status data to obtain a trained preliminary mental disorder assessment model. Based on the personal past medical history data and family mental illness history data, data processing is performed through a preset medical history detection model to obtain the latent factors for mental disorder. This factor can reflect the latent risk of mental disorder of the subject. Among them, the medical history detection model belongs to a neural network model, which is trained on a large amount of historical personal past medical history data and family mental illness history data to obtain a trained medical history detection model.

[0098] Please refer to Figure 5 , Figure 5 is a flowchart for obtaining the mental disorder patient degree parameter of the intelligent diagnosis and treatment method for mental disorder based on big data in some embodiments of the present invention. According to the embodiments of the present invention, the obtaining of the comprehensive status data of the target object includes clinical symptom data and psychological assessment data, and data processing is performed according to a preset mental disorder detection model to obtain the mental disorder patient degree parameter, including:

[0099] S51. Obtain the comprehensive status data of the target object, including clinical symptom data and psychological assessment data;

[0100] S52. The clinical symptom data includes symptom manifestation data and symptom duration data;

[0101] S53. The psychological assessment data includes self-rating inventory data and intelligence test data;

[0102] S54. Based on the symptom manifestation data and symptom duration data, combined with the symptom self-rating scale data and intelligence test data, data processing is performed through a preset mental disorder detection model to obtain a mental disorder patient degree parameter.

[0103] It should be noted that the diagnosis of mental disorders is a complex process and cannot be determined based on a single characteristic data alone. Instead, it requires comprehensive information from multiple aspects, including clinical symptoms, psychological assessment data, physiological index data, as well as personal history and family history, etc. for judgment. Therefore, further, it is necessary to collect the clinical symptom data and psychological assessment data of the target object to prepare for the next comprehensive assessment and diagnosis. Among them, the clinical symptom data includes symptom manifestation data and symptom duration data. For the symptom manifestation data, a detailed understanding of various mental symptoms of the patient is the key to diagnosis. Different types of mental disorders have their typical symptoms. For example, schizophrenia has positive symptoms such as hallucinations, delusions, thinking disorders, and abnormal behaviors, as well as negative symptoms such as emotional apathy, poverty of speech, and decreased will. And the duration of symptoms is also an important diagnostic basis. For example, the symptoms of depression usually need to last for at least two weeks or more, while the symptoms of schizophrenia generally need to reach one month or more, and there will be varying degrees of social function impairment during the disease process. The psychological assessment data includes symptom self-rating scale data and intelligence test data. For the symptom self-rating scale data, such as the Symptom Checklist-90 (SCL-90), it can allow patients to subjectively evaluate their own psychological symptoms and reflect the mental health status of patients from multiple dimensions. If the scores in multiple dimensions exceed the normal range, it is necessary to further evaluate whether there is a mental disorder. And for some patients who may have intellectual development problems or cognitive impairments, intelligence tests such as the Wechsler Adult Intelligence Scale (WAIS) can evaluate their intelligence level and help judge whether there is an intellectual disorder or other cognitive-related mental disorders. Then, based on the above data, data processing is performed through a preset mental disorder detection model to obtain a mental disorder patient degree parameter. Among them, the mental disorder detection model belongs to a neural network model and is trained based on a large amount of historical clinical symptom data and psychological assessment data to obtain a trained mental disorder detection model.

[0104] According to an embodiment of the present invention, the processing based on the mental disorder patient degree parameter in combination with the mental disorder patient inducing factor and the mental disorder latent factor to obtain mental disorder patient risk assessment data and judge the mental disorder symptoms of the target object includes:

[0105] Processing based on the mental disorder patient degree parameter in combination with the mental disorder patient inducing factor and the mental disorder latent factor through a preset mental disorder diagnosis model to obtain mental disorder patient risk assessment data;

[0106] Compare the mental disorder patient risk assessment data with the preset mental disorder patient risk level threshold to obtain the mental disorder symptom risk level;

[0107] Among them, the preset order risk level evaluation threshold includes the first preset mental disorder patient risk level threshold, the second preset mental disorder patient risk level threshold, and the third preset mental disorder patient risk level threshold, and the first preset mental disorder patient risk level threshold is less than the second preset mental disorder patient risk level threshold, and the second preset mental disorder patient risk level threshold is less than the third preset mental disorder patient risk level threshold;

[0108] If the mental disorder patient risk assessment data is less than or equal to the first preset mental disorder patient risk level threshold, it is determined that the mental disorder symptom of the target object is normal;

[0109] If the mental disorder patient risk assessment data is greater than the first preset mental disorder patient risk level threshold and less than or equal to the second preset mental disorder patient risk level threshold, it is determined that the mental disorder symptom of the target object is a mild symptom;

[0110] If the mental disorder patient risk assessment data is greater than the second preset mental disorder patient risk level threshold and less than or equal to the third preset mental disorder patient risk level threshold, it is determined that the mental disorder symptom of the target object is a moderate symptom;

[0111] If the mental disorder patient risk assessment data is greater than the third preset mental disorder patient risk level threshold, it is determined that the mental disorder symptom of the target object is a severe symptom.

[0112] It should be noted that, based on the mental disorder patient degree parameter, in combination with the mental disorder patient inducing factor and the mental disorder latent factor, through a preset mental disorder diagnosis model for processing, mental disorder patient risk assessment data is obtained. Among them, the mental disorder diagnosis model belongs to a neural network model, which is trained by a large number of historical mental disorder patient degree parameters, mental disorder patient inducing factors, and mental disorder latent factors for the initialized mental disorder diagnosis model to obtain a trained mental disorder diagnosis model. Then, the mental disorder patient risk assessment data is compared with the preset mental disorder patient risk level threshold to obtain the mental disorder symptom risk level. Among them, the preset order risk level evaluation threshold includes the first preset mental disorder patient risk level threshold, the second preset mental disorder patient risk level threshold, and the third preset mental disorder patient risk level threshold, and the first preset mental disorder patient risk level threshold is less than the second preset mental disorder patient risk level threshold, and the second preset mental disorder patient risk level threshold is less than the third preset mental disorder patient risk level threshold. For example, in this embodiment, the values of the first, second, and third preset mental disorder patient risk level thresholds are 0.25, 0.5, and 0.75 respectively. If the mental disorder patient risk assessment data is less than or equal to 0.25, it is diagnosed that the mental disorder symptom of the target object is normal. If the mental disorder patient risk assessment data is greater than 0.25 and less than or equal to 0.5, it is diagnosed that the mental disorder symptom of the target object is a mild symptom. If the mental disorder patient risk assessment data is greater than 0.5 and less than or equal to 0.75, it is diagnosed that the mental disorder symptom of the target object is a moderate symptom. If the mental disorder patient risk assessment data is greater than 0.75, it is diagnosed that the mental disorder symptom of the target object is a severe symptom.

[0113] According to an embodiment of the present invention, formulating a corresponding treatment plan according to the mental disorder symptom includes:

[0114] If the mental disorder symptom of the target object is normal, there is no need to formulate a treatment plan;

[0115] If the mental disorder symptom of the target object is a mild symptom, a corresponding treatment plan is formulated, and the plan includes psychotherapy and lifestyle adjustment;

[0116] If the mental disorder symptom of the target object is a moderate symptom, a corresponding treatment plan is formulated, and the plan includes drug treatment and psychotherapy;

[0117] If the mental disorder symptom of the target object is a severe symptom, a corresponding treatment plan is formulated, and the plan includes hospitalization treatment and rehabilitation treatment.

[0118] It should be noted that a corresponding treatment plan needs to be formulated for the mental disorder symptoms diagnosed in the target object. For example, if the target object is diagnosed with mild symptoms, it indicates that the symptoms are relatively mild and do not require drug treatment or hospitalization. Only psychological counseling is needed and bad living habits should be corrected.

[0119] According to an embodiment of the present invention, it further includes:

[0120] Obtain the performance index data of the preset mental disorder diagnosis model, including sensitivity data, specificity data, and Cohen's Kappa coefficient data;

[0121] Perform weighted processing according to the sensitivity data, specificity data, and Cohen's Kappa coefficient data to obtain a model diagnosis reliability parameter;

[0122] Compare the model diagnosis reliability parameter with a preset model diagnosis reliability threshold to obtain a second threshold comparison result;

[0123] If the second threshold comparison result is less than the preset threshold, the performance reliability of the preset mental disorder diagnosis model does not meet the standard and corresponding adjustments are required.

[0124] It should be noted that for the selection of the preset mental disorder diagnosis model, to ensure the reliability of the selected model, its reliability needs to be evaluated. Therefore, obtain the performance index data of the preset mental disorder diagnosis model, including sensitivity data, specificity data, and Cohen's Kappa coefficient data. Among them, sensitivity refers to the proportion of correctly identified positive cases (i.e., diagnosed as patients) in the group of truly diseased people, and specificity refers to the proportion of correctly identified cases in the true negative samples (i.e., the ability of the model to exclude healthy people). Then, perform weight ratio processing according to the above data to obtain a model diagnosis reliability parameter, and judge whether the performance reliability of the preset mental disorder diagnosis model meets the standard according to this parameter. For example, in this embodiment, assume that the model diagnosis reliability parameter is 0.9 and the preset threshold is 0.8, then it indicates that the performance reliability of the preset mental disorder diagnosis model meets the standard and no corresponding adjustments are required.

[0125] The third aspect of the present invention provides a readable storage medium, in which a program for the intelligent diagnosis and treatment method of mental disorders based on big data is stored. When the program for the intelligent diagnosis and treatment method of mental disorders based on big data is executed by a processor, the steps of the intelligent diagnosis and treatment method of mental disorders based on big data as described in any one of the above are realized.

[0126] The intelligent diagnosis and treatment device, method and medium for mental disorders based on big data disclosed by the present invention. The device includes a diagnosis device module and a treatment device module. The diagnosis device module includes a data acquisition module, a data processing module and an analysis and diagnosis module. The treatment device module includes a treatment plan formulation module, a treatment execution module and a rehabilitation follow-up module. The device executes the following method: obtaining the physiological characteristic data, psychological characteristic data, social characteristic data and medical history characteristic data of a target object, respectively performing data processing on the physiological characteristic data, psychological characteristic data, social characteristic data and medical history characteristic data through a preset preliminary mental disorder assessment model and a preset medical history detection model to obtain mental disorder inducing factors and mental disorder latent factors, obtaining the comprehensive status data of the target object, including clinical symptom data and psychological assessment data, performing data processing according to a preset mental disorder detection model to obtain a mental disorder patient degree parameter, processing according to the mental disorder patient degree parameter in combination with the mental disorder inducing factors and mental disorder latent factors to obtain mental disorder patient risk assessment data, and judging the mental disorder symptoms of the target object, and formulating a corresponding treatment plan according to the mental disorder symptoms, so as to realize the technology of intelligent diagnosis and treatment of mental disorders based on big data.

[0127] In several embodiments provided by the present invention, it should be understood that the disclosed device and method can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed with each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0128] The units described as separate components above may or may not be physically separated. The components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0129] In addition, each functional unit in the embodiments of the present invention can be all integrated in a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in a unit; the above integrated unit can be implemented in the form of hardware, or in the form of a hardware plus a software functional unit.

[0130] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a readable storage medium. When the program is executed, it performs the steps including those of the above method embodiments; and the foregoing storage medium includes: various media such as removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0131] Alternatively, if the above integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. And the foregoing storage medium includes: various media such as removable storage devices, ROM, RAM, magnetic disks, or optical discs that can store program codes.

Claims

1. A mental disorder intelligent diagnosis and treatment device based on big data, characterized in that Including: A diagnostic device module, including a data acquisition module, a data processing module, and an analysis and diagnosis module; The data acquisition module is used to acquire data of a target object; The data processing module is used to perform data integration, feature extraction, and data cleaning on the acquired data; The analysis and diagnosis module is used for model construction, data comparison analysis, and diagnostic decision-making.

2. The intelligent diagnosis and treatment device for mental disorders based on big data according to claim 1, wherein It also includes: A treatment device module, including a treatment plan formulation module, a treatment execution module, and a rehabilitation follow-up module; The treatment plan formulation module is used for plan generation and knowledge integration; The treatment execution module is used to execute patient treatment, including drug treatment, physical therapy, and psychotherapy; The rehabilitation follow-up module is used for rehabilitation monitoring, follow-up management, and data feedback.

3. A method for intelligent diagnosis and treatment of mental disorders based on big data, characterized in that, Including the following steps: Obtain the physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data of the target object; According to the physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data, perform data processing through a preset mental disorder preliminary assessment model and a preset medical history detection model respectively to obtain mental disorder patient inducing factors and mental disorder latent factors; Obtain the comprehensive status data of the target object, including clinical symptom data and psychological assessment data, and perform data processing according to a preset mental disorder detection model to obtain a mental disorder patient degree parameter; Process according to the mental disorder patient degree parameter in combination with the mental disorder patient inducing factors and mental disorder latent factors to obtain mental disorder patient risk assessment data, and judge the mental disorder symptoms of the target object; Formulate a corresponding treatment plan according to the mental disorder symptoms.

4. The intelligent diagnosis and treatment method for mental disorders based on big data according to claim 3, characterized in that, The obtaining of the physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data of the target object includes: Obtain the personal status information of the target object and extract personal status characteristic data, including physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data; The physiological characteristic data includes neuroimaging data, genetic gene data, and neurotransmitter level data; The psychological characteristic data includes personality trait data, cognitive function data, and psychological stress data; The social characteristic data includes life event data, social support data, and economic status data; The medical history characteristic data includes personal past medical history data and family mental history data.

5. The intelligent diagnosis and treatment method for mental disorders based on big data according to claim 4, characterized in that The performing of data processing according to the physiological characteristic data, psychological characteristic data, social characteristic data, and medical history characteristic data through a preset mental disorder preliminary assessment model and a preset medical history detection model respectively to obtain mental disorder patient inducing factors and mental disorder latent factors includes: According to the neuroimaging data, genetic gene data, neurotransmitter level data, personality trait data, cognitive function data, and psychological stress data, in combination with the life event data, social support data, and economic status data, perform data processing through a preset mental disorder preliminary assessment model to obtain mental disorder patient inducing factors; Process the personal past medical history data and family mental history data through a preset medical history detection model to obtain latent mental disorder factors.

6. The intelligent diagnosis and treatment method for mental disorders based on big data according to claim 5, wherein Obtain the comprehensive status data of the target object, including clinical symptom data and psychological assessment data, and process the data through a preset mental disorder detection model to obtain mental disorder patient degree parameters, including: Obtain the comprehensive status data of the target object, including clinical symptom data and psychological assessment data; The clinical symptom data includes symptom manifestation data and symptom duration data; The psychological assessment data includes self-rating inventory data and intelligence test data; Based on the symptom manifestation data and symptom duration data, combined with the self-rating inventory data and intelligence test data, process the data through a preset mental disorder detection model to obtain mental disorder patient degree parameters.

7. The method for intelligent diagnosis and treatment of mental disorders based on big data according to claim 6, characterized in that, Process the mental disorder patient degree parameters in combination with the mental disorder patient inducing factors and latent mental disorder factors to obtain mental disorder patient risk assessment data, and judge the mental disorder symptoms of the target object, including: Process the mental disorder patient degree parameters in combination with the mental disorder patient inducing factors and latent mental disorder factors through a preset mental disorder diagnosis model to obtain mental disorder patient risk assessment data; Compare the mental disorder patient risk assessment data with a preset mental disorder patient risk level threshold to obtain the mental disorder symptom risk level; Among them, the preset order risk level evaluation threshold includes a first preset mental disorder patient risk level threshold, a second preset mental disorder patient risk level threshold, and a third preset mental disorder patient risk level threshold, and the first preset mental disorder patient risk level threshold is less than the second preset mental disorder patient risk level threshold, and the second preset mental disorder patient risk level threshold is less than the third preset mental disorder patient risk level threshold; If the mental disorder patient risk assessment data is less than or equal to the first preset mental disorder patient risk level threshold, it is determined that the mental disorder symptoms of the target object are normal; If the mental disorder patient risk assessment data is greater than the first preset mental disorder patient risk level threshold and less than or equal to the second preset mental disorder patient risk level threshold, it is determined that the mental disorder symptoms of the target object are mild symptoms; If the mental disorder patient risk assessment data is greater than the second preset mental disorder patient risk level threshold and less than or equal to the third preset mental disorder patient risk level threshold, it is determined that the mental disorder symptoms of the target object are moderate symptoms; If the mental disorder patient risk assessment data is greater than the third preset mental disorder patient risk level threshold, it is determined that the mental disorder symptoms of the target object are severe symptoms.

8. The intelligent diagnosis and treatment method for mental disorders based on big data according to claim 7, characterized in that, Formulate a corresponding treatment plan according to the mental disorder symptoms, including: If the mental disorder symptoms of the target object are normal, there is no need to formulate a treatment plan; If the mental disorder symptoms of the target object are mild symptoms, formulate a corresponding treatment plan, and the plan includes psychotherapy and lifestyle adjustment; If the mental disorder symptoms of the target object are moderate symptoms, a corresponding treatment plan is formulated, and the plan includes drug treatment and psychotherapy; If the mental disorder symptoms of the target object are severe symptoms, a corresponding treatment plan is formulated, and the plan includes hospitalization treatment and rehabilitation treatment.

9. The intelligent diagnosis and treatment method for mental disorders based on big data according to claim 8, characterized in that It also includes: Obtain the performance index data of the preset mental disorder diagnosis model, including sensitivity data, specificity data, and Cohen's Kappa coefficient data; Perform weighted processing according to the sensitivity data, specificity data, and Cohen's Kappa coefficient data to obtain the model diagnosis reliability parameter; Compare the model diagnosis reliability parameter with the preset model diagnosis reliability threshold to obtain the second threshold comparison result; If the second threshold comparison result is less than the preset threshold, the performance reliability of the preset mental disorder diagnosis model does not meet the standard and corresponding adjustments are required.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a program for the intelligent diagnosis and treatment method of mental disorders based on big data. When the program for the intelligent diagnosis and treatment method of mental disorders based on big data is executed by a processor, the steps of the intelligent diagnosis and treatment method of mental disorders based on big data according to any one of claims 3 to 9 are implemented.

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