Automatic mental disorder diagnosis system

Abstract

The embodiment of the invention relates to an automatic mental disorder diagnosis system. The system comprises a storage module for storing a diagnosis problem list comprising a plurality of preset problems; the inquiry module is used for asking the target object on the basis of a plurality of preset questions in the diagnosis question list; the first data acquisition module is used for acquiring physiological electric signal data when the target object feeds back the question answer every time; the second data acquisition module is used for acquiring limb, expression and voice related data when the target object feeds back the question answer each time; the first diagnosis module takes the questions asked by the inquiry module every time and the physiological electric signal data, the limbs, the expressions and the voice related data when the target object feeds back the answers to the questions every time as input of a first model so as to output at least one symptom of the target object; and the second diagnosis module takes at least one symptom of the target object as input of a second model so as to output mental disorder analysis information of the target object.

Description

Technical Field

[0001] Multiple embodiments of this specification relate to the field of diagnostic technology, and specifically to an automated mental disorder diagnosis system. Background Art

[0002] Psychiatric diagnosis is a complex and rigorous task, involving a comprehensive assessment of the patient's mental state, psychological activities, behavioral performance, and other aspects. During the diagnosis process, psychiatrists need to use their professional knowledge, communicate with patients, and observe the patient's state during the communication process, and then conduct symptom assessments on the patient. Finally, based on the patient's symptoms, they can diagnose the patient's mental disorder information. There are currently many types of mental disorders, and different mental disorders have different symptoms, and may also have the same symptoms, which increases the difficulty of diagnosing mental illness. The above diagnostic model requires doctors to have rich clinical experience and high professional qualities to ensure the accuracy of the diagnosis.

[0003] At present, there are also some solutions for diagnosis based on big data. For example, the Chinese invention patent with application number 202210678147.0 discloses a big data mental disorder diagnosis system. During the diagnosis process, the doctor interviews the patient online and communicates with the patient face to face through the camera acquisition module and the portrait acquisition module. The patient's facial expression and emotions can be seen during the test. At the same time, the physiological sensor module detects the patient's physiological changes during the diagnosis process, increasing the basis for diagnosis and improving the accuracy of diagnosis. The mental disorder classification unit in this scheme includes a comparison module, a knowledge base, a real-time update module and a diagnosis module. The knowledge base stores the diagnosis and classification standards of mental disorders, which facilitates the system to perform comparative diagnosis based on the data obtained from the consultation. The real-time update module can continuously update the information in the knowledge base to increase the real-time and accuracy of the diagnosis. This scheme directly performs comparative diagnosis based on the data obtained from the consultation and the knowledge base to obtain the diagnosis results of mental disorders. In this diagnostic mode, the amount of data that needs to be processed by the comparison module for each diagnosis is relatively large, thereby reducing the efficiency and accuracy of the diagnosis. Summary of the invention

[0004] The embodiments of this specification provide an automated mental disorder diagnosis system that can automatically, accurately and efficiently diagnose mental disorders in patients.

[0005] The technical solution is as follows:

[0006] In a first aspect, an embodiment of this specification provides an automated mental disorder diagnosis system, including a storage module, an inquiry module, a first data acquisition module, and a second data acquisition module;

[0007] The storage module stores a diagnostic question list including a plurality of preset questions;

[0008] The inquiry module asks questions to the target object based on a plurality of preset questions in the diagnostic question list;

[0009] The first data acquisition module acquires physiological electrical signal data each time the target object provides feedback on an answer to a question;

[0010] The second data acquisition module acquires the target object's body movement data, target object's expression data, and target object's voice data each time the target object provides feedback on the answer to a question;

[0011] The first diagnostic module uses the physiological electrical signal data, the body movement data, the facial expression data, and the voice data of the target object each time the inquiry module asks a question and each time the target object provides feedback on the answer to a question as inputs to the first model to output at least one symptom of the target object;

[0012] The second diagnostic module uses at least one symptom of the target object as input to the second model to output mental disorder analysis information of the target object.

[0013] As a preferred solution, it also includes a third data acquisition module;

[0014] The third data acquisition module acquires basic information of the target object;

[0015] The first diagnostic module uses the target object's basic information, each question asked by the inquiry module, the target object's physiological electrical signal data each time the target object provides feedback on the answer to a question, the target object's body movement data, the target object's expression data, and the target object's voice data as inputs to the first model to output at least one symptom of the target object.

[0016] As a preferred solution, the inquiry module selects a corresponding virtual image according to the basic information of the target object and asks the target object a virtual interactive question.

[0017] As a preferred solution, the inquiry module includes a first processing unit and a questioning unit;

[0018] The questioning unit asks questions to the target object based on a plurality of preset questions in the diagnostic question list;

[0019] The first processing unit performs a deformation operation on the preset question to obtain a deformed question;

[0020] The questioning unit further asks questions to the target object based on the deformation question.

[0021] As a preferred solution, in the first processing unit, a deformation operation is performed on the preset question based on the physiological electrical signal data, the target object's body movement data, the target object's expression data, and the target object's voice data when the target object feeds back the answer to the question, and the deformation operation on the preset question includes changing the question length and question style of the preset question.

[0022] As a preferred solution, the first processing unit performs a preset number of deformation operations on the same preset problem.

[0023] As a preferred solution, the physiological electrical signal data acquired by the first data acquisition module each time the target object provides feedback on an answer to a question includes electroencephalogram data and electrocardiogram data.

[0024] As a preferred solution, the second data acquisition module includes a video data acquisition unit, a voice data acquisition unit, and a second processing unit;

[0025] The video data acquisition unit acquires the target object's video data each time the target object provides feedback on the answer to a question;

[0026] The voice data acquisition unit acquires the voice data of the target object each time the target object provides feedback on the answer to a question;

[0027] The second processing unit processes the target object video data each time the target object provides feedback on the answer to a question, so as to obtain the target object body movement data and target object expression data each time the target object provides feedback on the answer to a question.

[0028] As a preferred solution, it further comprises a fourth data acquisition module, and the storage module further stores a list of auxiliary questions for distinguishing types of mental disorders;

[0029] The inquiry module, when the mental disorder analysis information output by the second diagnosis module includes the mental disorder type that cannot be distinguished, asks questions to the auxiliary question-answering object of the target object based on the auxiliary question list;

[0030] The fourth data acquisition module acquires the feedback answers of the auxiliary question-answering object to the questions in the auxiliary question list;

[0031] The second diagnosis module also uses the auxiliary question-answering subject's feedback answers to the questions in the auxiliary question list and at least one symptom of the target object as input to the second model to output the target object's mental disorder analysis information again.

[0032] As a preferred solution, the storage module stores a plurality of auxiliary question lists for distinguishing different types of mental disorders;

[0033] When the mental disorder analysis information output by the second diagnosis module includes indistinguishable mental disorder types, the inquiry module selects a corresponding auxiliary question list based on the indistinguishable mental disorder types and asks questions to the auxiliary question-and-answer object of the target object.

[0034] In a second aspect, the embodiments of this specification provide an automated method for diagnosing mental disorders, comprising:

[0035] storing a diagnostic question list including a plurality of preset questions;

[0036] Asking questions to the target object based on multiple preset questions in the diagnostic question list;

[0037] Obtain physiological electrical signal data every time the target object provides feedback on the answer to a question;

[0038] Obtaining the target object's body movement data, target object's facial expression data, and target object's voice data each time the target object provides feedback on the answer to a question;

[0039] The physiological electrical signal data, the body movement data, the facial expression data, and the speech data of the target object when the inquiry module asks each question and the target object gives each feedback on the answer to the question as the input of the first model to output at least one symptom of the target object;

[0040] At least one symptom of the target object is used as an input to the second model to output mental disorder analysis information of the target object.

[0041] In a third aspect, an embodiment of this specification provides an electronic device, comprising a processor and a memory; the processor is connected to the memory; the memory is used to store executable program code; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps described in the second aspect of the above embodiment.

[0042] In a fourth aspect, an embodiment of this specification provides a computer storage medium, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the steps described in the second aspect of the above embodiment.

[0043] The beneficial effects brought by the technical solutions provided by some embodiments of this specification include at least:

[0044] The entire diagnostic process is divided into two diagnostic links. In the first link, the physiological electrical signal data, body movement data, expression data, and voice data of each question asked by the inquiry module and each time the target object feedbacks the answer to the question as the input of the first model to output at least one symptom of the target object; in the second link, at least one symptom of the target object is used as the input of the second model to output the mental disorder analysis information of the target object. This improves the accuracy and specificity of the first model and the second model. In the first link, by integrating multimodal data, including physiological electrical signals, body movements, expressions, and voice data, the first model can more comprehensively capture the comprehensive response of the target object when answering questions, thereby more accurately identifying potential symptoms. This combination of multi-dimensional data not only enhances the reliability of diagnosis, but also reduces the bias that may be caused by a single data source. In the second link, the second model uses the symptom information output by the first model to conduct a deeper analysis of mental disorders. This hierarchical diagnostic method allows each model to focus on the problem-solving part it is best at, thereby improving the overall diagnostic efficiency and accuracy.

[0045] The preset questions are deformed based on the target object's body movement data, target object expression data, and target object voice data when the target object provides feedback on the answers to the questions. The deforming operation on the preset questions includes changing the length and style of the preset questions, and asking questions to the target object based on the deformed questions, thereby achieving better consultation results.

[0046] Because some mental disorders have similar symptoms, the inquiry module also asks questions to the auxiliary question-answering subjects of the target object based on the auxiliary question list (the auxiliary question-answering subjects can be understood as people with normal mental states such as the target object's family members and friends) when the mental disorder analysis information output by the second diagnosis module includes indistinguishable mental disorder types; the second diagnosis module also uses the feedback answers of the auxiliary question-answering subjects to the questions in the auxiliary question list and at least one symptom of the target object as the input of the second model to output the mental disorder analysis information of the target object again. This improves the accuracy of the mental disorder analysis information. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1It is a schematic diagram of the structure of an automated mental disorder diagnosis system provided in an embodiment of this specification.

[0049] Figure 2 It is a structural diagram of another automated mental disorder diagnosis system provided in an embodiment of this specification.

[0050] Figure 3 It is a structural diagram of another automated mental disorder diagnosis system provided in an embodiment of this specification.

[0051] Figure 4 It is a flowchart of an automated method for diagnosing mental disorders provided in an embodiment of this specification.

[0052] Figure 5 It is a structural schematic diagram of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of this specification will be described clearly and completely below in conjunction with the drawings in the embodiments of this specification.

[0054] The terms "first", "second", "third", etc. in the description and claims of this specification and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0055] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present specification. Various processes or components may be appropriately omitted, substituted or added to each example. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted or combined. In addition, features described with respect to some examples may be combined in other examples.

[0056] Reference Figure 1 , which is a schematic diagram of the structure of an automated mental disorder diagnosis system provided in one or more embodiments of this specification.

[0057] One or more embodiments of this specification provide an automated mental disorder diagnosis system, which may include at least a storage module 101, an inquiry module 102, a first data acquisition module 103, a second data acquisition module 104, a first diagnosis module 105, and a second diagnosis module 106;

[0058] The storage module 101 stores a diagnostic question list including a plurality of preset questions;

[0059] The inquiry module 102 asks questions to the target object based on a plurality of preset questions in the diagnostic question list;

[0060] The first data acquisition module 103 acquires physiological electrical signal data each time the target object provides feedback on an answer to a question;

[0061] The second data acquisition module 104 acquires the body movement data, expression data, and voice data of the target object each time the target object provides feedback on the answer to a question;

[0062] The first diagnosis module 105 uses the physiological electrical signal data, the body movement data, the facial expression data, and the voice data of the target object each time the inquiry module asks a question and each time the target object provides a feedback answer as an input to the first model to output at least one symptom of the target object;

[0063] The second diagnostic module 106 uses at least one symptom of the target object as an input of the second model to output mental disorder analysis information of the target object.

[0064] It should be noted that the target object is the object that needs to be diagnosed with a mental disorder.

[0065] It should also be noted that the symptoms output by the first diagnosis module 105 may be, but are not limited to: delusion symptoms, hallucination symptoms, language and thought disorder symptoms, emotional instability symptoms, desire loss symptoms, inattention symptoms, memory loss symptoms, etc. The mental disorder analysis information output by the second diagnosis module 106 may be, but is not limited to, the type of mental disorder and the content of the follow-up treatment recommendations, and the type of mental disorder may be, but is not limited to, schizophrenia, bipolar disorder, anxiety disorder, stress-related disorder, mania and depression, where each mental disorder has at least one of the above symptoms, and different symptoms may exist between mental disorders, or the same symptoms may exist.

[0066] The first model and the second model are both obtained by training the neural network model through a large number of training data samples.

[0067] For the first model, the training data samples used need to include physiological electrical signal data, body movement data, facial expression data, voice data and symptom information of the sample subject each time the sample subject provides feedback on the answer to a question.

[0068] For the second model, the training data samples used need to include symptom information and mental disorder information of the sample subjects, and may also include corresponding treatment recommendation information.

[0069] The specific training process of the first model and the second model is prior art, so it will not be described in detail in this specification.

[0070] An automated mental disorder diagnosis system provided by multiple embodiments of this specification divides the entire diagnosis process into two diagnostic links. In the first link, the physiological electrical signal data, the target object body movement data, the target object expression data, and the target object voice data of each question asked by the inquiry module 102 and each time the target object feeds back the answer to the question as the input of the first model to output at least one symptom of the target object; in the second link, at least one symptom of the target object is used as the input of the second model to output the mental disorder analysis information of the target object. Thus, the accuracy and specificity of the first model and the second model are improved. In the first link, by integrating multimodal data, including physiological electrical signals, body movements, expressions and voice data, the first model can more comprehensively capture the comprehensive response of the target object when answering questions, thereby more accurately identifying potential symptoms. This combination of multidimensional data not only enhances the reliability of diagnosis, but also reduces the deviation that may be caused by a single data source. In the second link, the second model uses the symptom information output by the first model to conduct a deeper analysis of mental disorders. This hierarchical diagnostic method enables each model to focus on the problem-solving part it is best at, thereby improving the overall diagnostic efficiency and accuracy.

[0071] The physiological electrical signal data acquired by the first data acquisition module 103 each time the target object provides feedback on an answer to a question includes electroencephalogram data and electrocardiogram data.

[0072] The second data acquisition module 104 includes a video data acquisition unit, a voice data acquisition unit, and a second processing unit;

[0073] The video data acquisition unit acquires the target object's video data each time the target object provides feedback on the answer to a question;

[0074] The voice data acquisition unit acquires the voice data of the target object each time the target object provides feedback on the answer to a question;

[0075] The second processing unit processes the target object video data each time the target object provides feedback on the answer to a question, so as to obtain the target object body movement data and target object expression data each time the target object provides feedback on the answer to a question.

[0076] Understandably, basic information about the target subject, such as age, gender, educational background, family environment, etc., has an important impact on the diagnosis of mental symptoms. For example, some mental symptoms are more common in people of a certain age group, and stress and conflict in the family environment may also exacerbate certain mental symptoms.

[0077] Therefore, refer to Figure 2 As shown, in multiple embodiments of this specification, the automated mental disorder diagnosis system further includes a third data acquisition module 201;

[0078] The third data acquisition module 201 acquires basic information of the target object;

[0079] The first diagnostic module 105 uses the basic information of the target object, the questions asked by the inquiry module 102 each time, the physiological electrical signal data of the target object each time the target object provides feedback on the answer to the question, the target object's body movement data, the target object's expression data, and the target object's voice data as inputs to the first model to output at least one symptom of the target object.

[0080] In multiple embodiments of the present specification, the inquiry module 102 selects a corresponding virtual image according to basic information of the target object to conduct virtual interactive questions to the target object.

[0081] It is understandable that for target objects with different basic information, corresponding virtual images can be selected for interactive questioning to improve the authenticity of the consultation process. In addition to the selection of virtual images, the dialogue environment can also be selected.

[0082] In various embodiments of this specification, the inquiry module 102 includes a first processing unit, a questioning unit;

[0083] The questioning unit asks questions to the target object based on a plurality of preset questions in the diagnostic question list;

[0084] The first processing unit performs a deformation operation on the preset question to obtain a deformed question;

[0085] The questioning unit further asks questions to the target object based on the deformation question.

[0086] It can be understood that in order to improve the accuracy of the medical consultation, in addition to the multiple preset questions in the diagnostic question list, in multiple embodiments of the present specification, the preset questions are also deformed to obtain deformed questions, and the questioning unit further asks questions to the target object based on the deformed questions.

[0087] In multiple embodiments of the present specification, in the first processing unit, a deformation operation is performed on the preset question based on the physiological electrical signal data, the target object's body movement data, the target object's expression data, and the target object's voice data when the target object provides feedback on the answer to the question, and the deformation operation on the preset question includes changing the question length and question style of the preset question.

[0088] It is understandable that in order to improve the diagnostic effect, when the preset questions are deformed, the preset questions are deformed based on the physiological electrical signal data, target object body movement data, target object expression data, and target object voice data when the target object provides feedback on the answers to the questions.

[0089] For example, when it is judged that the user is in a highly nervous state based on physiological electrical signal data, target object body movement data, target object expression data, and target object voice data, the preset questions can be deformed to simplify the content of the questions or change the way the questions are expressed to reduce the other party's mental stress. If subsequent questions are asked based on the deformed questions and the target object is still in a highly nervous state, it means that the target user does have certain symptoms.

[0090] In order to improve the diagnosis efficiency, in multiple embodiments of the present specification, the first processing unit performs a preset number of deformation operations for the same preset question, which may be but is not limited to 3 times or 5 times.

[0091] It is understandable that for a target subject with a mental disorder, the symptoms output by the first model are usually more accurate, but since mental disorders may have different symptoms or the same symptoms (for example, both depression and bipolar disorder may show symptoms of depression; some mental disorders may even show exactly the same symptoms), the second model may not be able to accurately determine which mental disorder the target subject has even if the corresponding symptom information is input.

[0092] However, it is also understandable that although both depressive disorder and bipolar disorder have symptoms of low mood, there are certain differences in the manifestation of symptoms, such as:

[0093] Depressive disorders: The patient's low mood is usually persistent and may last for weeks, months or even longer.

[0094] Bipolar disorder: Patients experience low mood only during the episode of the disorder and do not last long.

[0095] Based on the above problems, refer to Figure 3 As shown, in multiple embodiments of the present specification, the automated mental disorder diagnosis system further includes a fourth data acquisition module 301, and the storage module 101 further stores a list of auxiliary questions for distinguishing types of mental disorders;

[0096] The inquiry module 102 asks questions to the auxiliary question-answering object of the target object based on the auxiliary question list when the mental disorder analysis information output by the second diagnosis module 106 includes an indistinguishable type of mental disorder;

[0097] The fourth data acquisition module 301 acquires the feedback answers of the auxiliary question-answering object to the questions in the auxiliary question list;

[0098] The second diagnosis module 106 also uses the auxiliary question-answering subject's feedback answers to the questions in the auxiliary question list and at least one symptom of the target subject as inputs to the second model to output the target subject's mental disorder analysis information again.

[0099] It should be noted that the auxiliary question-and-answer objects can be understood as people with normal mental state, such as family members and friends of the target object. By asking the auxiliary question-and-answer objects some questions that can distinguish the types of disorders for the second time, the second model can be assisted in the accurate output of mental disorder analysis information.

[0100] For example, when the types of mental disorders that cannot be distinguished are depressive disorder and bipolar disorder, the auxiliary question-answering object can be asked whether the target object's low mood symptoms are persistent or sporadic. If it is persistent, the second model will be guided to output that the target object is depressive disorder. Otherwise, the second model will be guided to output that the target object is bipolar disorder.

[0101] Furthermore, it can be understood that, based on the solution described in this embodiment, for the training of the second model, its training samples also need to include auxiliary question and answer content.

[0102] It should be noted that the target subjects are not asked further questions here because they may not be able to provide absolutely correct answers to these auxiliary questions that serve to distinguish obstacles.

[0103] In multiple embodiments of the present specification, the storage module 101 stores multiple auxiliary question lists respectively used to distinguish different types of mental disorders;

[0104] When the mental disorder analysis information output by the second diagnosis module 106 includes indistinguishable mental disorder types, the inquiry module 102 selects a corresponding auxiliary question list based on the indistinguishable mental disorder types and asks questions to the auxiliary question-and-answer object of the target object.

[0105] It is understandable that the needs for distinguishing different types of mental disorders and the content of questions asked to the auxiliary question-and-answer objects are different. Therefore, in multiple embodiments of the present specification, different auxiliary question lists are set for multiple indistinguishable types of mental disorders to adapt to the needs for distinguishing different types of mental disorders.

[0106] Reference Figure 4 As shown, Figure 4 A flowchart of an automated method for diagnosing mental disorders provided in an embodiment of this specification may include at least the following steps:

[0107] Step 402, storing a diagnostic question list including a plurality of preset questions;

[0108] Step 404: Ask the target object questions based on a plurality of preset questions in the diagnostic question list;

[0109] Step 406, obtaining physiological electrical signal data each time the target subject provides feedback on the answer to a question;

[0110] Step 408, obtaining the target object's body movement data, target object's expression data, and target object's voice data each time the target object provides feedback on the answer to a question;

[0111] Step 410: using the physiological electrical signal data, the body movement data, the facial expression data, and the speech data of the target object each time the inquiry module asks a question and each time the target object provides feedback on the answer to a question as inputs to the first model, so as to output at least one symptom of the target object;

[0112] Step 412: Use at least one symptom of the target object as input to the second model to output mental disorder analysis information of the target object.

[0113] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the mental disorder diagnosis method embodiment, since it is basically similar to the mental disorder diagnosis system embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the mental disorder diagnosis system embodiment.

[0114] See also Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification is shown.

[0115] like Figure 5 As shown, the electronic device 500 may include: at least one processor 501 , at least one network interface 504 , a user interface 503 , a memory 505 , and at least one communication bus 502 .

[0116] The communication bus 502 may be used to realize the connection and communication among the above-mentioned components.

[0117] The user interface 503 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0118] The network interface 504 may include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0119] Among them, the processor 501 may include one or more processing cores. The processor 501 uses various interfaces and lines to connect the various parts of the entire electronic device 500, and executes various functions and processes data of the electronic device 500 by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 505. Optionally, the processor 501 can be implemented in at least one hardware form of DSP, FPGA, and PLC. The processor 501 can integrate one or a combination of CPU, GPU, modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 501, and it can be implemented separately through a chip.

[0120] Among them, the memory 505 may include RAM or ROM. Optionally, the memory 505 includes a non-transient computer-readable medium. The memory 505 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 505 may also be at least one storage device located away from the aforementioned processor 501. The memory 505 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a mental disorder diagnosis application. The processor 501 may be used to call the mental disorder diagnosis program stored in the memory 505 and execute the steps of the mental disorder diagnosis method mentioned in the above-mentioned embodiment.

[0121] The embodiments of this specification also provide a computer-readable storage medium, which stores instructions, and when the instructions are executed on a computer or a processor, the computer or the processor executes one or more steps in the above-mentioned mental disorder diagnosis method embodiment. If the component modules of the above-mentioned electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0122] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of this specification is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. that contains one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0123] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes. In the absence of conflict, the technical features in this embodiment and the implementation scheme can be combined arbitrarily.

[0124] The embodiments described above are merely preferred embodiments of this specification and are not intended to limit the scope of this specification. Without departing from the design spirit of this specification, various modifications and improvements made to the technical solutions of this specification by ordinary technicians in this field should fall within the scope of protection determined by the claims of this specification.

Claims

1. An automated system for diagnosing mental disorders, characterized in that: It includes a storage module, an inquiry module, a first data acquisition module, a second data acquisition module, a first diagnosis module, and a second diagnosis module; The storage module stores a diagnostic question list including a plurality of preset questions; The inquiry module asks questions to the target object based on a plurality of preset questions in the diagnostic question list; The first data acquisition module acquires physiological electrical signal data each time the target object provides feedback on an answer to a question; The second data acquisition module acquires the target object's body movement data, target object's expression data, and target object's voice data each time the target object provides feedback on the answer to a question; The first diagnostic module uses the physiological electrical signal data, the body movement data, the facial expression data, and the voice data of the target object each time the inquiry module asks a question and each time the target object provides feedback on the answer to a question as inputs to the first model to output at least one symptom of the target object; The second diagnostic module uses at least one symptom of the target object as input to the second model to output mental disorder analysis information of the target object.

2. An automated mental disorder diagnosis system according to claim 1, characterized in that: Also includes a third data acquisition module; The third data acquisition module acquires basic information of the target object; The first diagnostic module uses the basic information of the target object, the questions asked by the inquiry module each time, the physiological electrical signal data of the target object each time the target object provides feedback on the answer to a question, the target object's body movement data, the target object's expression data, and the target object's voice data as inputs to the first model to output at least one symptom of the target object.

3. An automated mental disorder diagnosis system according to claim 1, characterized in that: The inquiry module selects a corresponding virtual image according to the basic information of the target object and asks the target object a virtual interactive question.

4. An automated mental disorder diagnosis system according to claim 1, characterized in that: The inquiry module includes a first processing unit and a questioning unit; The questioning unit asks questions to the target object based on a plurality of preset questions in the diagnostic question list; The first processing unit performs a deformation operation on the preset question to obtain a deformed question; The questioning unit further asks questions to the target object based on the deformation question.

5. An automated mental disorder diagnosis system according to claim 4, characterized in that: In the first processing unit, the preset question is deformed based on the target object's body movement data, target object expression data, and target object voice data when the target object provides feedback on the answer to the question, and the deformation operation on the preset question includes changing the question length and question style of the preset question.

6. An automated mental disorder diagnosis system according to claim 4, characterized in that: The first processing unit performs a preset number of deformation operations on the same preset problem.

7. An automated mental disorder diagnosis system according to claim 1, characterized in that: The physiological electrical signal data acquired by the first data acquisition module each time the target object provides feedback on an answer to a question includes electroencephalogram (EEG) data and electrocardiogram (ECG) data.

8. An automated mental disorder diagnosis system according to claim 1, characterized in that: The second data acquisition module includes a video data acquisition unit, a voice data acquisition unit, and a second processing unit; The video data acquisition unit acquires the target object's video data each time the target object provides feedback on the answer to a question; The voice data acquisition unit acquires the voice data of the target object each time the target object provides feedback on the answer to a question; The second processing unit processes the target object video data each time the target object provides feedback on the answer to a question, so as to obtain the target object body movement data and target object expression data each time the target object provides feedback on the answer to a question.

9. An automated mental disorder diagnosis system according to claim 1, characterized in that: A fourth data acquisition module is also included, and the storage module further stores a list of auxiliary questions for distinguishing types of mental disorders; The inquiry module, when the mental disorder analysis information output by the second diagnosis module includes the mental disorder type that cannot be distinguished, asks questions to the auxiliary question-answering object of the target object based on the auxiliary question list; The fourth data acquisition module acquires the feedback answers of the auxiliary question-answering object to the questions in the auxiliary question list; The second diagnosis module also uses the auxiliary question-answering subject's feedback answers to the questions in the auxiliary question list and at least one symptom of the target object as input to the second model to output the target object's mental disorder analysis information again.

10. An automated mental disorder diagnosis system according to claim 9, characterized in that: The storage module stores a plurality of auxiliary question lists respectively used to distinguish different types of mental disorders; When the mental disorder analysis information output by the second diagnosis module includes indistinguishable mental disorder types, the inquiry module selects a corresponding auxiliary question list based on the indistinguishable mental disorder types and asks questions to the auxiliary question-and-answer object of the target object.

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