Diagnosis method and device based on artificial intelligence, electronic equipment and storage medium

Abstract

The invention discloses a diagnosis method and device based on artificial intelligence, electronic equipment and a storage medium, the method and device are applied to the electronic equipment, and specifically, digital processing is carried out on a selected medical standard to obtain a medical digital standard; establishing a disease diagnosis mathematical model based on a medical digital standard; and in response to a diagnosis request of a user, performing diagnosis processing based on data input by the user, and obtaining and outputting a diagnosis result. According to the scheme, the disease of the patient is diagnosed based on the artificial intelligence method, no medical staff is needed to participate, and the diagnosis result given by the medical staff is more objective and reasonable compared with the medical staff.

Description

Technical Field

[0001] The present application relates to the field of medical information technology, and more specifically, to an artificial intelligence-based diagnostic method, device, electronic device, and storage medium. Background Art

[0002] Modern medicine has developed into a logically rigorous and comprehensive diagnostic and treatment system. Through the diligent efforts of medical professionals over hundreds of years, humanity has mastered diagnostic and treatment knowledge and methods that can cover the diagnosis and treatment of nearly all common diseases. However, due to the natural limitations of human learning and cognitive analysis, it is impossible for every medical professional to be fully aware of the corresponding disease. As a result, patients sometimes cannot receive an effective diagnosis when seeking medical treatment, which creates difficulties in subsequent treatment. Summary of the Invention

[0003] In view of this, the present application provides an artificial intelligence-based diagnostic method, device, electronic device and storage medium for diagnosing a patient's condition based on an artificial intelligence method and providing a more objective and reasonable diagnostic result.

[0004] In order to achieve the above objectives, the following solutions are proposed:

[0005] An artificial intelligence-based diagnostic method, applied to electronic equipment, comprising the steps of:

[0006] Digitally processing the selected medical standards to obtain digital medical standards;

[0007] Establishing a disease diagnosis mathematical model based on the medical digitalization standard;

[0008] In response to the user's diagnosis request, the disease diagnosis mathematical model is used to perform diagnosis processing on the data input by the user to obtain and output the diagnosis result.

[0009] Optionally, the step of digitally processing the selected medical standard to obtain a digital medical standard comprises the following steps:

[0010] Determine the digital name based on the disease classification standards;

[0011] Collect authoritative textual standards corresponding to said digitized names;

[0012] The authoritative text standard is digitized to obtain the medical digitization standard.

[0013] Optionally, the authoritative text standard is digitized to obtain the medical digitization standard. The steps include:

[0014] Classifying the authoritative text standards by dimension to obtain multi-dimensional text standard digital elements;

[0015] Decomposing the standard digital elements of the text into digitized elements;

[0016] Classifying and encoding the digital elements to obtain multiple groups of data items;

[0017] The multiple groups of data items are iteratively labeled to obtain the medical digitization standard.

[0018] Optionally, the establishing of a disease diagnosis mathematical model based on the medical digitalization standard comprises the following steps:

[0019] logically annotating the medical digitization standards based on expert knowledge;

[0020] The model is constructed based on the annotated medical digital standards to obtain the disease diagnosis mathematical model.

[0021] Optionally, the method of establishing a disease diagnosis mathematical model based on the medical digitalization standard further includes the following steps:

[0022] The disease diagnosis mathematical model is tested and optimized based on the test results.

[0023] A diagnostic device based on artificial intelligence, applied to electronic equipment, comprising:

[0024] A digital processing module is configured to perform digital processing on the selected medical standard to obtain a medical digital standard;

[0025] a model building module, configured to establish a disease diagnosis mathematical model based on the medical digitalization standard;

[0026] The diagnosis execution module is configured to respond to the user's diagnosis request, perform diagnosis processing on the data input by the user using the disease diagnosis mathematical model, and obtain and output the diagnosis result.

[0027] Optionally, the digital processing module includes:

[0028] a name determination unit configured to determine a digital name according to a disease classification standard;

[0029] a text collection unit configured to collect authoritative text standards corresponding to the digitized name;

[0030] The standard processing unit is configured to perform digital processing on the authoritative text standard to obtain the medical digital standard.

[0031] Optionally, the model building module includes:

[0032] a logic annotation unit, configured to perform logic annotation on the medical digitization standard based on expert knowledge;

[0033] The construction execution unit is configured to construct a model based on the marked medical digitalization standard to obtain the disease diagnosis mathematical model.

[0034] The test optimization unit is configured to test the disease diagnosis mathematical model and optimize it based on the test results.

[0035] An electronic device comprising at least one processor and a memory connected to the processor, wherein:

[0036] The memory is used to store computer programs or instructions;

[0037] The processor is configured to execute the computer program or instruction so as to enable the electronic device to implement the diagnostic method described above.

[0038] A computer-readable storage medium is applied to an electronic device, wherein the storage medium carries one or more computer programs, and the one or more computer programs can be executed by the electronic device, thereby enabling the electronic device to implement the diagnostic method described above.

[0039] As can be seen from the above technical solutions, this application discloses an artificial intelligence-based diagnostic method, device, electronic device, and storage medium. The method and device are applied to electronic devices and specifically perform digital processing on selected medical standards to obtain medical digital standards; establish a disease diagnosis mathematical model based on the medical digital standards; and respond to user diagnostic requests, perform diagnostic processing based on user-entered data, and obtain and output diagnostic results. Because this solution uses artificial intelligence methods to diagnose patients' conditions, it does not require the participation of medical personnel, and the diagnostic results provided are more objective and reasonable than those provided by medical personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] Figure 1 This is a flowchart of an artificial intelligence-based diagnostic method according to an embodiment of the present application;

[0042] Figure 2 This is a flow chart of digital processing of medical standards according to an embodiment of the present application;

[0043] Figure 3 This is a flowchart of an embodiment of the present application for establishing a disease diagnosis mathematical model based on medical digitalization standards;

[0044] Figure 4 This is another flowchart of establishing a disease diagnosis mathematical model based on medical digitalization standards according to an embodiment of the present application;

[0045] Figure 5 This is a block diagram of an artificial intelligence-based diagnostic device according to an embodiment of the present application;

[0046] Figure 6 This is a block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0048] In traditional society, the evolution and continuation of medical text data occurred within a binary world: 1. The spiritual world (the carbon-based human consciousness system) and 2. The material world (elementary particles and document systems). In the information society, the evolution and continuation of medical digital artificial intelligence data occurs within a ternary world: 1. The spiritual world (the carbon-based human consciousness system), 2. The material world (elementary particles and document systems), and 3. The information world (silicon-based artificial intelligence information systems).

[0049] After entering the information society, silicon-based artificial intelligence information systems have developed rapidly, the clerical system will be quickly iterated, and the memory of the human consciousness system will also be degraded. If the data in the artificial intelligence information system cannot be interpreted in text, the human consciousness system will also be degraded and iterated like the clerical system.

[0050] This system digitizes, logicalizes, mathematically models, and allows for textual interpretation of medical standards. This extends the lifecycle of the document system and slows the degradation of human consciousness. Currently, the behavioral standards of AI big data models are not textually interpretable, and the evolution and continuation of medical data standards may be interrupted at the textual system level. Given this, this application proposes the following technical solution.

[0051] Figure 1 This is a flowchart of an artificial intelligence-based diagnostic method according to an embodiment of the present application.

[0052] like Figure 1As shown, the diagnostic method provided in this embodiment is applied to an electronic device for diagnosing a user based on artificial intelligence. The electronic device can be understood as a computer, server, or cloud platform with digital computing and information processing capabilities. The diagnostic method includes the following steps:

[0053] S1. Digitally process the selected medical standards to obtain digital medical standards.

[0054] Generally speaking, in order to improve the diagnostic effect, one or several diseases can be selected for diagnosis, which is both targeted. This embodiment uses hypertension and diabetes as examples to illustrate the technical solution of this application. The process of implementing digital processing here is as follows: Figure 2 shown.

[0055] S101. Determine the digital name based on the disease classification standards.

[0056] For example, select hypertension, diabetes, etc.

[0057] S102. Collect authoritative text standards corresponding to digital names.

[0058] That is, based on the above digital names, the contents of authoritative text materials such as documents, textbooks, papers, etc. that correspond to them are selected.

[0059] S103. Digitize authoritative text standards.

[0060] Medical digital standards are obtained by digitizing authoritative text standards. The specific processing process is as follows:

[0061] First, authoritative text standards are categorized by dimension to obtain multi-dimensional text standard digital elements. The dimensional classification methods here include multi-dimensional classification of medical data, classification of all indicators of medical data, and dynamic classification of medical data by time period.

[0062] 1. Multi-dimensional classification of medical data includes:

[0063] (1) General situation;

[0064] (2) past medical history;

[0065] (3) vaccination history;

[0066] (4) Family genetic history;

[0067] (5) Marital history;

[0068] (6) menstrual and reproductive history;

[0069] (7) social behavior;

[0070] (8) Lifestyle habits;

[0071] (9) Clinical symptoms;

[0072] (10) psychological and cognitive function evaluation;

[0073] (11) Laboratory science;

[0074] (12) Radiographic (imaging) examination;

[0075] (13) Electrophysiological examination;

[0076] (14) Nuclear medicine;

[0077] (15) Pathological diagnosis;

[0078] (16) Other physical instrument inspections.

[0079] 2. The classification of all indicators of medical data includes: (for example: laboratory medicine). (1) Medical statistical classification items (4 categories);

[0080] (2) Chinese and English report name items (2 categories);

[0081] (3) Test result items (category 1);

[0082] (4) Unit item (category 1);

[0083] (5) Reference scope items (category 1);

[0084] (6) Container items (Class 1);

[0085] (7) Specimen items (Category 1);

[0086] (8) Related equipment items (Category 1);

[0087] (9) Department of inspection (Category 1);

[0088] (10) Prompt items (category 1);

[0089] (11) Search item (category 1).

[0090] 3. The dynamic classification of medical data by time period is shown in the following table:

[0091]

[0092]

[0093] Then, the standard digital elements of the text are decomposed into digital elements, including multiple dimensions, full indicators, and indicator value ranges or threshold ranges or time ranges, etc. The details are shown in the following table:

[0094]

[0095]

[0096] Then, the digital elements are classified and coded to obtain multiple groups of data items, specifically the classification and coding of their dimensions, indicators, indicator values, indicator value ranges, indicator threshold ranges, and indicator time ranges. Specifically, the following contents are included:

[0097] 1. Medical order classification and coding scheme:

[0098] (1) Classification and coding of hospital financial charge items (21 items) (financial statistics classification);

[0099] (2) Classification and coding of the contents of the medical record homepage (38 items) (business statistics classification);

[0100] (3) Comparison and classification coding between the hospital's financial charge item classification and the medical record homepage content classification;

[0101] (4) Classification and coding of drug orders: classification and coding of drug names, classification and coding of usage and dosage, classification and coding of drug warehouses and pharmacies, and classification and coding of drug statistics;

[0102] (5) Classification and coding of medical examination orders: classification and coding of name and group name, container, specimen, department for examination, relevant equipment, standard value range, application form, report form, statistical classification, etc.;

[0103] (6) Check the classification and coding of medical orders: classification and coding of name and group name, operation department, relevant equipment, application form, report form, statistical classification, etc.;

[0104] (7) Classification and coding of surgical orders: classification and coding of surgical name and group name, surgical high-value consumables, surgical statistical classification, etc.;

[0105] (8) Other medical order classification codes: classification codes such as name and group name, statistical classification, etc.

[0106] 2. Classification and coding scheme for medical records:

[0107] (1) General classification and coding;

[0108] (2) classification and coding of past medical history;

[0109] (3) Classification and coding of vaccination history;

[0110] (4) Classification and coding of family genetic history;

[0111] (5) Classification and coding of marital history;

[0112] (6) Classification and coding of menstrual and reproductive history;

[0113] (7) Classification and coding of social behaviors;

[0114] (8) Classification and coding of living habits;

[0115] (9) Classification and coding of clinical symptoms;

[0116] (10) Coding of psychological and cognitive function evaluation;

[0117] (11) Outpatient disease diagnosis reason code;

[0118] (12) ICD-10 code for the disease diagnosis upon admission.

[0119] 3. Medical multi-dimensional, full-index, dynamic time-segment data coding scheme:

[0120] (1) General classification and coding;

[0121] (2) classification and coding of past medical history;

[0122] (3) Classification and coding of vaccination history;

[0123] (4) Classification and coding of family genetic history;

[0124] (5) Marital history classification code;

[0125] (6) Classification and coding of menstrual and reproductive history;

[0126] (7) Social behavior classification and coding;

[0127] (8) Classification and coding of living habits;

[0128] (9) Clinical symptom classification and coding;

[0129] (10) Classification and coding of psychological and cognitive function evaluation;

[0130] (11) Classification and coding of laboratory science;

[0131] (12) Classification and coding of radiographic (imaging) examinations;

[0132] (13) Classification and coding of electrophysiological examination;

[0133] (14) Nuclear medicine classification code;

[0134] (15) Pathological diagnosis classification code;

[0135] (16) Other physical instrument inspection classification codes.

[0136] Finally, multiple groups of data items are iteratively labeled to obtain the above-mentioned medical digitization standards.

[0137] Medical disease diagnostic standards have current practicality. It is necessary to remove outdated content in a timely manner and introduce exploratory content with caution. Therefore, continuous iterative annotation is required, as shown in the following table.

[0138] Iteration annotation time Digital marking of numerical values ​​for hypertension classification criteria 20220508 High normal blood pressure Systolic blood pressure 120-139 mmHg Diastolic blood pressure 80-89 mmHg 20220508 Hypertension level 1 hypertension Systolic blood pressure 140-159 mmHg Diastolic blood pressure 90-99 mmHg 20220508 Hypertension level 2 hypertension Systolic blood pressure 160-179 mmHg Diastolic blood pressure 100-109 mmHg 20220508 Hypertension level 3 Systolic blood pressure ≥ 180 mmHg Diastolic blood pressure ≥110 mmHg

[0139] S2. Establish a mathematical model for disease diagnosis based on medical digitalization standards.

[0140] After obtaining the medical digitalization standard, a model is constructed based on the medical digitalization standard to obtain a disease diagnosis mathematical model for diagnosing the disease. Figure 3 The specific process is as follows.

[0141] S201. Logically label medical digitization standards based on expert knowledge.

[0142] That is, based on the professional knowledge of medical experts, artificial intelligence experts, and medical information experts, logical annotation is performed on the iteratively annotated medical digitalization standards. The following table lists one annotation result:

[0143]

[0144]

[0145] S202. Construct a model based on annotated medical digital standards.

[0146] Specifically, the model is constructed based on the medical digitalization standard after iterative labeling and logical labeling processing, so as to obtain the mathematical model for disease diagnosis.

[0147] The following table is an example of the digitization, logicization, and mathematical modeling of hypertension grading / high-risk grouping standard data. Medical professionals can read it in text and use it as a reference for the path of digitizing text medical standards.

[0148]

[0149]

[0150] In addition, in a specific implementation of the embodiment of the present application, the following steps are also included: Figure 4 shown.

[0151] S203. Test and optimize the medical diagnosis mathematical model.

[0152] That is, the medical diagnosis model constructed above is tested using standard data, the model is optimized according to the test results, and then tested again, optimized again... Through iterative testing and optimization, the accuracy of the model can meet the needs of users.

[0153] S3. Perform diagnostic operations according to the user's diagnostic request.

[0154] That is, when a user issues a diagnosis request through a terminal or a corresponding interface, the medical diagnosis mathematical model processes the user's input data to obtain a diagnosis result, which is then fed back to the terminal or interface to meet the user's diagnosis needs.

[0155] As can be seen from the above technical solution, this embodiment provides an artificial intelligence-based diagnostic method, which is applied to electronic devices. Specifically, the method digitizes selected medical standards to obtain digital medical standards; establishes a disease diagnosis mathematical model based on the digital medical standards; and responds to user diagnostic requests, performs diagnostic processing based on user-entered data, and obtains and outputs diagnostic results. Because this solution uses artificial intelligence to diagnose patient conditions, it does not require medical personnel to participate, and the diagnostic results provided are more objective and reasonable than those provided by medical personnel.

[0156] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0157] Although the operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or in a sequential order.Multitasking and parallel processing may be advantageous under certain circumstances.

[0158] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0159] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0160] Figure 5 This is a block diagram of an artificial intelligence-based diagnostic device according to an embodiment of the present application.

[0161] like Figure 5 As shown, the diagnostic device provided in this embodiment is applied with an electronic device for realizing diagnosis of the user based on artificial intelligence. The electronic device can be understood as a computer, server or cloud platform with digital computing and information processing capabilities. The diagnostic device includes a digital processing module 10, a model building module 20 and a diagnostic execution module 30.

[0162] The digital processing module is used to digitally process the selected medical standards to obtain medical digital standards.

[0163] Generally speaking, in order to improve the diagnostic effect, one or several diseases can be selected for diagnosis, which is both targeted and specific. This embodiment uses hypertension and diabetes as examples to illustrate the technical solution of this application. The digital processing module of this application includes a name determination unit, a text collection unit, and a standard processing unit.

[0164] The name determination unit is used to determine the digital name according to the disease classification standard.

[0165] For example, select hypertension, diabetes, etc.

[0166] The text collection unit is used to collect authoritative text standards corresponding to digital names.

[0167] That is, based on the above digital names, the contents of authoritative text materials such as documents, textbooks, papers, etc. that correspond to them are selected.

[0168] The standards processing unit is used to digitize authoritative text standards.

[0169] The medical digital standard is obtained by digitizing the authoritative text standard. The model building module is used to establish a disease diagnosis mathematical model based on the medical digital standard.

[0170] After obtaining the medical digitalization standard, a model is constructed based on the medical digitalization standard to obtain a disease diagnosis mathematical model for diagnosing the disease. This module includes a logic annotation unit, a construction execution unit, and a test optimization unit.

[0171] The logic annotation unit is used to perform logic annotation on medical digitization standards based on expert knowledge.

[0172] That is, based on the professional knowledge of medical experts, artificial intelligence experts, and medical information experts, logical annotation is performed on the iteratively annotated medical digitalization standards. The following table lists one annotation result:

[0173]

[0174] The construction execution unit is used to construct the model based on the annotated medical digitalization standards.

[0175] Specifically, the model is constructed based on the medical digitalization standard after iterative labeling and logical labeling processing, so as to obtain the mathematical model for disease diagnosis.

[0176] The test and optimization unit is used to test and optimize medical diagnosis mathematical models.

[0177] That is, the medical diagnosis model constructed above is tested using standard data, the model is optimized according to the test results, and then tested again, optimized again... Through iterative testing and optimization, the accuracy of the model can meet the needs of users.

[0178] The diagnosis execution module is used to execute the diagnosis operation according to the user's diagnosis request.

[0179] That is, when a user issues a diagnosis request through a terminal or a corresponding interface, the medical diagnosis mathematical model processes the user's input data to obtain a diagnosis result, which is then fed back to the terminal or interface to meet the user's diagnosis needs.

[0180] As can be seen from the above technical solution, this embodiment provides an artificial intelligence-based diagnostic device, which is applied to electronic devices. Specifically, the device digitizes selected medical standards to obtain digital medical standards; establishes a disease diagnosis mathematical model based on the digital medical standards; and responds to user diagnostic requests, performs diagnostic processing based on user-entered data, and obtains and outputs diagnostic results. Because this solution uses artificial intelligence to diagnose patient conditions, it does not require medical personnel, and the diagnostic results provided are more objective and reasonable than those provided by medical personnel.

[0181] The units involved in the embodiments described in this disclosure may be implemented in software or hardware. In some cases, the name of a unit does not limit the unit itself. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses."

[0182] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0183] Figure 6 This is a block diagram of an electronic device according to an embodiment of the present application.

[0184] Reference below Figure 6 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. This electronic device is merely an example and should not limit the functionality and scope of use of the embodiments of the present disclosure.

[0185] The electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory ROM 602 or a program loaded from an input device 606 into a random access memory RAM 603. Various programs and data required for the operation of the electronic device are also stored in the RAM. The processing device, ROM, and RAM are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0186] Typically, the following devices may be connected to the I / O interface: input devices such as a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 607 such as a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 608 such as a magnetic tape, hard disk, etc.; and communication devices 609. Communication devices 609 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various devices, it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0187] This embodiment also provides a computer-readable storage medium embodiment.

[0188] The computer-readable storage medium is applied to an electronic device and carries one or more computer programs. When executed by the electronic device, the one or more computer programs cause the electronic device to digitize selected medical standards to obtain digital medical standards; establish a mathematical model for disease diagnosis based on the digital medical standards; and respond to a user's diagnostic request by performing diagnostic processing based on user-entered data to obtain and output a diagnostic result. Because this solution uses artificial intelligence to diagnose a patient's condition, it does not require medical personnel, and the diagnostic results provided are more objective and reasonable than those provided by medical personnel.

[0189] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0190] In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

[0191] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0192] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0193] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0194] The technical solution provided by the present invention is introduced in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A diagnostic method based on artificial intelligence, applied to electronic equipment, characterized in that: The diagnostic method comprises the steps of: Digitally processing the selected medical standards to obtain digital medical standards; Establishing a disease diagnosis mathematical model based on the medical digitalization standard; In response to the user's diagnosis request, the disease diagnosis mathematical model is used to perform diagnosis processing on the data input by the user to obtain and output the diagnosis result.

2. The diagnostic method according to claim 1, wherein The step of digitalizing the selected medical standard to obtain the medical digital standard comprises the following steps: Determine the digital name according to the disease classification standards; Collect authoritative textual standards corresponding to said digitized names; The authoritative text standard is digitized to obtain the medical digitization standard.

3. The diagnostic method according to claim 2, wherein The method of digitizing the authoritative text standard to obtain the medical digitization standard comprises the following steps: Classifying the authoritative text standards by dimension to obtain multi-dimensional text standard digital elements; Decomposing the standard digital elements of the text into digitized elements; Classifying and encoding the digital elements to obtain multiple groups of data items; The multiple groups of data items are iteratively labeled to obtain the medical digitization standard.

4. The diagnostic method according to claim 1, wherein The method of establishing a disease diagnosis mathematical model based on the medical digitalization standard comprises the following steps: logically annotating the medical digitization standards based on expert knowledge; The model is constructed based on the annotated medical digital standards to obtain the disease diagnosis mathematical model.

5. The diagnostic method according to claim 4, wherein The method of establishing a disease diagnosis mathematical model based on the medical digitalization standard further includes the steps of: The disease diagnosis mathematical model is tested and optimized based on the test results.

6. A diagnostic device based on artificial intelligence, applied to electronic equipment, characterized in that: The diagnostic device comprises: A digital processing module is configured to perform digital processing on the selected medical standard to obtain a medical digital standard; a model building module, configured to establish a disease diagnosis mathematical model based on the medical digitalization standard; The diagnosis execution module is configured to respond to the user's diagnosis request, perform diagnosis processing on the data input by the user using the disease diagnosis mathematical model, and obtain and output the diagnosis result.

7. The diagnostic device according to claim 6, wherein The digital processing module includes: a name determination unit configured to determine a digital name according to a disease classification standard; a text collection unit configured to collect authoritative text standards corresponding to the digitized name; The standard processing unit is configured to perform digital processing on the authoritative text standard to obtain the medical digital standard.

8. The diagnostic device according to claim 6, wherein The model building module includes: a logic annotation unit, configured to perform logic annotation on the medical digitization standard based on expert knowledge; A construction execution unit is configured to construct a model based on the annotated medical digitalization standard to obtain the disease diagnosis mathematical model; The test optimization unit is configured to test the disease diagnosis mathematical model and optimize it based on the test results.

9. An electronic device, characterized in that: The electronic device comprises at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs or instructions; The processor is configured to execute the computer program or instruction so as to enable the electronic device to implement the diagnostic method according to any one of claims 1 to 5.

10. A computer-readable storage medium, applied to an electronic device, characterized in that: The storage medium carries one or more computer programs, and the one or more computer programs can be executed by the electronic device, so that the electronic device implements the diagnostic method according to any one of claims 1 to 5.

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