Neuroimmunoassay methods, devices, systems, and apparatuses

Abstract

Embodiments of the present application disclose a kind of neuroimmunology detection analysis method, device, system and equipment, wherein, method includes: obtaining the detection result of at least one neuroimmunology detection item associated with target object;Detection result is input into preset neuroimmunology detection result analysis model, obtain the course classification result corresponding to detection result, and read target neuroimmunology detection information according to course classification result;Based on detection result, course classification result and target neuroimmunology detection information, generate target neuroimmunology detection report, and send target neuroimmunology detection report to target client.The technical scheme of the present application embodiment solves the problem that the content of the detection report of third-party detection agency is single, realizes the rich report content according to detection result, can provide detailed analysis report and reference information related to detection result for medical staff and patient, assist in the interpretation of detection result in clinic.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of medical data processing and analysis technology, and in particular to a neuroimmunological detection and analysis method, apparatus, system and device. Background Technology

[0002] Neuroimmune diseases are autoimmune diseases that affect the nervous system. Their diagnosis and treatment involve many tests, and the analysis is usually outsourced to third-party testing institutions.

[0003] The analysis of neuroimmunological test results relies on the doctor's knowledge base and relevant clinical experience. However, the existing case samples for neuroimmunological disease testing are relatively small, and the reference data and related research are not yet perfect. Currently, the test results from third-party testing institutions only provide numerical results. The efficiency and accuracy of doctors relying solely on their own clinical experience to analyze the test results need to be improved. Summary of the Invention

[0004] This invention provides a neuroimmunological detection and analysis method, apparatus, system, and equipment, which solves the problem of limited content in third-party testing reports. It enables the generation of rich report content based on test results, providing medical staff and patients with detailed analysis reports and reference information related to the test results, and assisting in the clinical interpretation of test results.

[0005] In a first aspect, embodiments of the present invention provide a method for neuroimmunological detection and analysis, the method comprising:

[0006] Obtain the test results of at least one neuroimmunological test associated with the target object;

[0007] The test results are input into the preset neuroimmune test result analysis model to obtain the disease segment classification results corresponding to the test results, and the target neuroimmune test information is read according to the disease segment classification results;

[0008] Based on the test results, disease segment classification results, and target neuroimmunological test information, a target neuroimmunological test report is generated and sent to the target client.

[0009] Secondly, embodiments of the present invention provide a neuroimmunological detection and analysis device, the device comprising:

[0010] The test result acquisition module is used to acquire the test results of at least one neuroimmunological test item associated with the target object;

[0011] The detection result analysis module is used to input the detection results into the preset neuroimmune detection result analysis model to obtain the disease segment classification result corresponding to the detection result, and read the target neuroimmune detection information based on the disease segment classification result;

[0012] The test report generation module is used to generate a target neuroimmune test report based on the test results, disease segment classification results, and target neuroimmune test information, and send the target neuroimmune test report to the target client.

[0013] Thirdly, embodiments of the present invention also provide a neuroimmunological detection and analysis system, the system comprising:

[0014] A neuroimmunological detection server and at least one neuroimmunological detection client;

[0015] Among them, the neuroimmunological detection client is used to determine the target neuroimmunological detection items for the target object based on the user's input information, and send the target neuroimmunological detection item information to the neuroimmunological detection server;

[0016] The neuroimmunological detection server is used to obtain the detection results of the target object based on the target neuroimmunological detection project information, and to complete the neuroimmunological detection analysis method provided in any embodiment based on the detection results, so as to provide neuroimmunological detection analysis results to users and target objects.

[0017] Fourthly, embodiments of the present invention also provide a server device, the server device comprising:

[0018] One or more processors;

[0019] Memory, used to store one or more programs;

[0020] When the one or more programs are executed by the one or more processors, the one or more processors implement the neuroimmunological detection and analysis method provided in any embodiment of the present invention.

[0021] Fifthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the neuroimmunological detection and analysis method provided in any embodiment of the present invention.

[0022] The embodiments of the above invention have the following advantages or beneficial effects:

[0023] In this embodiment of the invention, by acquiring the test results of at least one neuroimmunological test associated with the target object, inputting the test results into a preset neuroimmunological test result analysis model, obtaining the disease segment classification result corresponding to the test result, and reading the target neuroimmunological test information based on the disease segment classification result, generating a target neuroimmunological test report based on the test results, disease segment classification result, and target neuroimmunological test information, and sending the target neuroimmunological test report to the target client, this invention solves the problem of the limited content of test reports from third-party testing institutions, and realizes the generation of rich report content based on test results. It can provide medical staff and patients with detailed analysis reports and reference information related to the test results, and assist in the clinical interpretation of test results. Attached Figure Description

[0024] Figure 1 This is a flowchart of a neuroimmunological detection and analysis method provided in an embodiment of the present invention;

[0025] Figure 2 This is a flowchart of another neuroimmunological detection and analysis method provided in an embodiment of the present invention;

[0026] Figure 3 This is a structural block diagram of a neuroimmunological detection and analysis device provided in an embodiment of the present invention;

[0027] Figure 4 This is a structural block diagram of a neuroimmunological detection and analysis system provided in an embodiment of the present invention;

[0028] Figure 5 This is a structural block diagram of a server device provided in an embodiment of the present invention. Detailed Implementation

[0029] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0030] Furthermore, it should be noted that the acquisition, storage, use, and processing of data in this application's technical solution all comply with the relevant provisions of national laws and regulations.

[0031] Figure 1This is a flowchart of a neuroimmunological detection and analysis method provided by an embodiment of the present invention. This embodiment is applicable to scenarios where third-party testing institutions perform neuroimmunological testing according to the testing requirements of hospitals and generate reports based on the test results. This method can be executed by a neuroimmunological detection and analysis device, which can be implemented in hardware and / or software and configured in a neuroimmunological detection and analysis system, or in a server device.

[0032] like Figure 1 As shown, the neuroimmunological detection and analysis method includes the following steps:

[0033] S110. Obtain the test results of at least one neuroimmunological test associated with the target object.

[0034] The target group refers to the individual who needs to undergo neuroimmunological testing, such as a patient with a neuroimmunological disease.

[0035] Neuroimmunological testing can include clinical serum and cerebrospinal fluid (CSF) tests, as well as immune function tests. Clinical serum tests primarily use immunological methods to detect the concentration of various antibodies or substances in serum. Results may include autoimmune antibodies related to autoimmune encephalomyelitis, Lambert-Eaton myasthenia gravis, neuromyelitis optica and its spectrum disorders, and stiff-person syndrome. Correspondingly, test results may include serum C-reactive protein (CRP) levels, blood virus-specific antibody levels, anti-acetylcholine receptor antibody levels in blood and CSF, CSF purulent meningitis pathogen antigen and antibody levels, CSF tuberculosis immunological tests, blood and CSF syphilis immunological tests, and CSF gamma globulin and immunoglobulin levels. The antibodies can be VGKC complex antibodies (including VGKC, CASPR2, etc.), specific serum markers such as VGCC antibodies, AChR antibodies and Musk antibodies, titin antibodies, specific serum markers such as AQP4 antibodies, and GAD antibodies. Immune function testing can include the detection of cytokine storms, autoantibodies, lymphocyte subsets, and immunoelectrophoresis. Correspondingly, the test results can include the detection results of antinuclear antibody profiles, antiplatelet antibodies, and antineutrophil cytoplasmic antibodies, etc.

[0036] Understandably, the at least one neuroimmunological test associated with the target individual can be determined by a doctor based on information such as the target individual's clinical symptoms or chief complaint. Once the doctor has determined the test, the neuroimmunological testing and analysis system can identify the test performed on the target individual and obtain the corresponding test results upon completion. Obtaining the test results of at least one neuroimmunological test associated with the target individual is a fundamental requirement for performing neuroimmunological testing and analysis.

[0037] S120. Input the test results into the preset neuroimmune test result analysis model to obtain the disease segment classification results corresponding to the test results, and read the target neuroimmune test information according to the disease segment classification results.

[0038] Among them, the preset neuroimmunological test result analysis model is a pre-trained neural network model used to interpret the test results of the neuroimmunological test items of the target object and determine the disease segment corresponding to the test results.

[0039] The disease course refers to different stages in the progression of the disease, which can be divided into the incubation period, prodromal period, symptomatic period, and outcome period. Further, the incubation period refers to the time from when the pathogen invades the body until the earliest clinical symptoms appear, during which there are no symptoms. Patients in the prodromal period begin to experience clinical symptoms, such as fatigue, headache, and mild fever, but there are no specific clinical symptoms or signs yet. Close observation is necessary during this period for early diagnosis and timely intervention. Patients in the symptomatic period develop various disease-specific symptoms and signs. The outcome period refers to the segment where the condition worsens or improves, or spreads or lessens.

[0040] Among them, the target neuroimmunological detection information is reference information that can be related to the disease course classification results, onset site and case of the target object.

[0041] Among them, the site of onset is the site of onset corresponding to the neuroimmune disease of the target subject, such as the central nervous system, peripheral nervous system, neuromuscular junction, and immune-related muscle lesions.

[0042] Understandably, the test results need to be input into a pre-trained, preset neuroimmunological result analysis model. This model matches the test results with the corresponding disease segment classification to obtain the corresponding disease segment classification result. Then, based on the disease segment classification result, it can further read reference information related to the disease segment classification result, the onset site, and the case of the target object.

[0043] Furthermore, the training process for the pre-defined neural immune detection result analysis model includes:

[0044] First, the historical neuroimmunological test results and corresponding disease segment classification labels in the preset neuroimmunological database are used as training sample data for the model.

[0045] The pre-built neuroimmunological database is a pre-established database used to analyze pre-defined neuroimmunological test results. This database may include historical data on immune test results and classification labels corresponding to disease progression segments.

[0046] Historical neuroimmunological test results and corresponding disease segment classification labels in a pre-set neuroimmunological database can be used as training sample data for the model. The neuroimmunological test result analysis model can then be further trained based on the training sample data.

[0047] Then, the model sample data is input into the initial neuroimmunological detection result analysis model for model training until the model loss function converges, thus obtaining the preset neuroimmunological detection result analysis model.

[0048] The model sample data refers to the sample data used to train the initial neuroimmunological detection result analysis model. For example, it may be the detection results of at least one neuroimmunological detection item associated with the target object.

[0049] In deep learning, a loss function maps the values ​​of a random event or its related random variables to non-negative real numbers to represent the "risk" or "loss" of that random event. Each sample, after being processed by the model, yields a predicted value; the difference between the predicted value and the true value is the loss. Essentially, a loss function is a type of function that calculates the difference between the predicted and true values, and it is further refined using libraries such as PyTorch and TensorFlow to form specific functions. In deep learning, minimizing the loss function helps the model reach convergence, reducing the error in the model's predictions. Therefore, different loss functions have a significant impact on the model. Commonly used loss functions include L1Loss, MSELoss, and CrossEntropyLoss. In applications, the loss function is often used as a learning criterion related to the optimization problem; that is, the model is solved and evaluated by minimizing the loss function, serving as a guiding function for adjusting classifier weights.

[0050] Understandably, during the training process of the pre-set neuroimmunological detection result analysis model, each model sample data, after passing through the initial neuroimmunological detection result analysis model, will yield a predicted disease segment classification. The difference between the predicted disease segment classification and the actual disease segment classification becomes the loss. During model training, the loss function converges, the model training is complete, and the pre-set neuroimmunological detection result analysis model is obtained.

[0051] S130. Based on the detection results, disease segment classification results, and target neuroimmunological detection information, generate a target neuroimmunological detection report and send the target neuroimmunological detection report to the target client.

[0052] The target neuroimmunological detection report may include detection results associated with the target subject, disease segment classification results determined by a pre-set neuroimmunological detection result analysis model, and target neuroimmunological detection information related to the disease segment classification results.

[0053] The target client can be a platform such as an outpatient clinic, WeChat official account, mini program, or app. It can provide patients and outpatient doctors with a visual user interface, realize data transmission, add search function, search for disease keywords or patient information, and obtain the target neuroimmunological test report of relevant test items by simply entering keywords, which is convenient for patients and outpatient doctors to find.

[0054] Understandably, the target neuroimmunological test report is generated by obtaining the test results of at least one neuroimmunological test associated with the target object, the disease segment classification results, and the target neuroimmunological test information. The target neuroimmunological test report is then sent to outpatient clinics, WeChat official accounts, mini programs, and apps to facilitate timely access to test reports by medical staff and patients.

[0055] In one optional implementation, before inputting the detection results into a preset neuroimmunological detection result analysis model, the method further includes:

[0056] First, obtain the primary interpretation results of the test results, and then determine whether to perform secondary interpretation based on the primary interpretation results.

[0057] Optionally, the primary interpretation results may include clinical symptoms, relevant antibody types, and relevant indicator values.

[0058] Optional clinical symptoms may include visual impairment, numbness and weakness of limbs, bowel and bladder dysfunction, unsteady gait, dizziness and headache, fever and altered consciousness.

[0059] Optionally, relevant antibody types may include antibodies associated with neuromyelitis optica spectrum disorders: AQP4, MOG, MBP, AQP1, and Flotillin-1 / 2; antibodies associated with paraneoplastic neurosis: Hu, Yo, Ri, CV2, PNMA2 (Ma-2 / Ta), amphiphysin, recoverin, SOX1, titin, Zic4, GAD65, and Tr (DNER); antibodies associated with autoimmune meningoencephalitis: GFAP; and antibodies associated with autoimmune encephalitis: NMDAR, AMPAR1, AMPAR2, LGI1, GABABR, CASPR2, and I... Antibodies associated with immune-mediated peripheral neuropathy include gLON5, DPPX, GlyRa1, GABAARa1, GABAARb3, mGluR5, D2R, Neurexin3α, and GAD65; GABAARa1, GABAARb3, GD1a, GD1b, GD2, GD3, GT1a, GT1b, and GQ1b; GABAARa1, AchR, Musk, Titin, SOX-1, LRP4, VGCC, and RYR; and GABAARa1, RO-52, EJ, PL-12, PL-7, SRP, JO-1, and PM-Scl75.

[0060] Optionally, the relevant indicator values ​​are the indicator values ​​in the test results, which correspond to the corresponding disease stage classification. For example, they could be the cerebrospinal fluid titer of NMDAR and the blood sample titer of NMDAR.

[0061] Understandably, interpreting test results and providing further information related to the results can help doctors or patients analyze and understand the results.

[0062] Optionally, the primary interpretation can be automatically obtained through a pre-set interpretation model; alternatively, relevant customer service personnel can interpret the test results through the neuroimmunological detection server and upload the primary interpretation results to the neuroimmunological detection server.

[0063] Specifically, the test results are automatically interpreted through a pre-set interpretation model, or relevant customer service personnel perform a first-level interpretation through the neuroimmunological testing server to obtain the first-level interpretation result. Based on the first-level interpretation result, it is determined whether to perform a second-level interpretation of the test results.

[0064] Furthermore, based on the results of the primary interpretation, it is determined whether a secondary interpretation of the test results is required, including:

[0065] Determine whether the primary interpretation results correspond to the corresponding clinical symptoms; and / or,

[0066] Determine whether the antibody type in the primary interpretation results is a novel or rare antibody; and / or,

[0067] Determine if any abnormal values ​​appear in the first-level interpretation results.

[0068] Specifically, the analysis involves checking whether the textual description of clinical symptoms in the primary interpretation results matches the clinical symptoms corresponding to the pre-defined categories, and / or whether the antibody type in the primary interpretation results matches the antibody type corresponding to the pre-defined categories, and / or whether there are any abnormal values ​​in the antibody type in the primary interpretation results compared to the data indicators corresponding to the pre-defined categories. For example, it could be NMDAR positive in cerebrospinal fluid, and generally the cerebrospinal fluid titer is higher than the blood sample titer.

[0069] Then, when it is determined that secondary interpretation is required, the results of the secondary interpretation are obtained. The target neuroimmunological detection report is then updated based on the results of the secondary interpretation.

[0070] Understandably, interpreting test results at multiple levels can improve the accuracy of test result analysis.

[0071] Optionally, the secondary interpretation can be automatically performed using a pre-set interpretation model to obtain the secondary interpretation result; alternatively, relevant experts can perform secondary interpretation of the test results through a neuroimmunological detection client, upload the secondary interpretation result to the neuroimmunological detection server, and then obtain the secondary interpretation result through the neuroimmunological detection server.

[0072] Specifically, the test results are automatically interpreted through a pre-set interpretation model, or relevant experts perform secondary interpretation through the neuroimmunological testing server to obtain secondary interpretation results. The target neuroimmunological testing report is then updated based on these secondary interpretation results. The secondary interpretation results can be added to the target neuroimmunological testing report, and additional relevant case information or literature content can be matched and added to the report based on the secondary interpretation results, thus enabling the report to be updated.

[0073] The technical solution of this embodiment obtains the test results of at least one neuroimmunological test associated with the target object, inputs the test results into a preset neuroimmunological test result analysis model, obtains the disease segment classification result corresponding to the test result, reads the target neuroimmunological test information based on the disease segment classification result, generates a target neuroimmunological test report based on the test results, disease segment classification result and target neuroimmunological test information, and sends the target neuroimmunological test report to the target client. This solves the problem of insufficient result analysis by third-party testing institutions, realizes intelligent analysis of test results, and can provide medical staff and patients with detailed analysis reports and reference information related to the test results, assisting in the clinical interpretation of test results.

[0074] Figure 2 This is a flowchart of another neuroimmunological detection and analysis method provided by an embodiment of the present invention. Based on the above embodiments, this embodiment further describes the process of reading target neuroimmunological detection information according to the disease segment classification results. This embodiment is applicable to the analysis of neuroimmunological detection results by hospitals through third-party testing institutions. This method can be executed by a neuroimmunological detection and analysis device, which can be implemented in hardware and / or software, or configured in a server device. Figure 2 As shown, a neuroimmunological detection and analysis method includes the following steps:

[0075] S210. Obtain the test results of at least one neuroimmunological test item associated with the target object, input the test results into the preset neuroimmunological test result analysis model, and obtain the disease segment classification result corresponding to the test results.

[0076] S220. Based on the index information of the disease course segment classification results, match the case course data associated with the test results in the preset neuroimmune database.

[0077] Based on the basic information of the target object and the test results, a clustering model can be established to classify the patient's disease course into data segments, and the identifier of each segment can be used as index information.

[0078] The basic information may include gender, age, history of preceding infection, clinical manifestations, imaging manifestations, EDSS (Expanded Disability Status Scale), and prognosis.

[0079] Specifically, based on the target subject's test results, gender, age, history of preceding infection, clinical manifestations, imaging manifestations, EDSS, and prognosis, a clustering model is used to classify the patient's disease course into data segments, thereby achieving disease course segment classification. The identifier of each segment is used as index information, and the generated segment index information is compared with a preset neuroimmunological database to obtain the case course related to the target subject's test results.

[0080] S230. Based on the disease course segment classification results, determine the node position of the test result in the classification tree corresponding to the case disease course data.

[0081] Specifically, based on the disease course segment classification results, the position of the current target object's test result in the ordered multi-way tree corresponding to the case disease course data, as well as the parent node of that position, are determined.

[0082] The process of constructing the classification tree includes: first, generating corresponding index information for historical neuroimmunological test results and corresponding disease segment classification results in the preset neuroimmunological database; then, generating a target ordered multi-branch tree based on the disease segment and index information, wherein the weight value of each node branch of the target ordered multi-branch tree represents the correlation coefficient between disease segments.

[0083] Multi-way trees exist to facilitate convenient and fast searches. Under certain data conditions, the height and width of the tree are mutually constrained. The height of the tree represents an unavoidable lower bound on the search time. Typically, binary trees are too tall for practical applications; multi-way trees simplify the description of data relationships. The simplest binary tree, while easy to implement, lacks practical value. A parent node in a multi-way tree can have multiple child nodes, but each child node still follows the rule of having only one parent node; the order of nodes in an ordered multi-way tree cannot be arbitrarily changed. In the target ordered multi-way tree, each node represents a disease segment, and the weight values ​​of its branches represent the correlation coefficient between disease segments, in order to output one or more more matching recommended case disease segments.

[0084] Each node in an ordered multi-way tree describes a disease course segment and its index information. The set of all nodes from the first-level child nodes to any terminal node of that node is at least a subset of the relevant case disease course. After determining the position of the current target object's detection result in the ordered multi-way tree corresponding to the case disease course data, the probability of disease development in the ordered multi-way tree, the probability of the disease course segment represented by the right child node must not be less than the probability of its left sibling node. Of course, multiple settings can be made depending on the situation.

[0085] Understandably, for historical neuroimmunological test results in the pre-defined neuroimmunological database, a clustering model can be used to classify the disease course corresponding to the historical neuroimmunological test results into data segments to achieve disease course segment classification, and the classification identifier of each disease course segment can be used as the corresponding index information. Then, a target ordered multi-way tree is generated based on the disease course segment and index information, and the nodes in the tree are traversed in depth and / or breadth according to the pre-defined order of each node to determine the node position of the test result in the classification tree corresponding to the case disease course data.

[0086] S240. Read the disease course data of the target case according to the node position, as the target neuroimmunological detection information.

[0087] Among them, the target case course data is determined from the index information in a pre-set neuroimmunological database, which identifies the case course data related to the test results of the target subjects.

[0088] Specifically, the case course data related to the test results of the target object is read from the node position in the classification tree corresponding to the case course data, and used as the target neuroimmunological detection information.

[0089] S250. Match target references in the preset neuroimmune database based on keywords in the disease course segment classification results, and use them as target neuroimmune detection information.

[0090] Specifically, multiple first tags are added to the literature in the preset neuroimmune database, and multiple second tags are added to the paragraphs containing the first tags. At least one keyword is generated from the text of the second tags. Based on the keyword in the disease course segment classification results, similarity analysis is performed with the keyword, second tag, and first tag in sequence to match one or more references as target references. The target references are sorted in sequence according to their similarity with the first tag, second tag, and keyword, and the target references are output in order as target neuroimmune detection information.

[0091] S260 generates a target neuroimmune detection report based on the detection results, disease segment classification results, and target neuroimmune detection information, and sends the target neuroimmune detection report to the target client.

[0092] The technical solution of this embodiment generates a target ordered multi-branch tree, reads the case course data related to the detection results of the target object at the node position in the classification tree corresponding to the case course data, and uses it as the target neuroimmune detection information. Moreover, it matches the target references in the preset neuroimmune database according to the keywords in the course segment classification results, and uses them as the content of the target neuroimmune detection report, thereby improving the efficiency and accuracy of diagnosis and treatment.

[0093] Figure 3 This is a structural block diagram of a neuroimmunological detection and analysis device provided in an embodiment of the present invention. This embodiment can be applied to the analysis of neuroimmunological detection results by a hospital through a third-party testing institution. The device can be implemented by software and / or hardware and integrated into a server device with application development capabilities.

[0094] like Figure 3 As shown, the device includes: a test result acquisition module 301, a test result analysis module 302, and a test report generation module 303.

[0095] The detection result acquisition module 301 is used to acquire the detection results of at least one neuroimmunological detection item associated with the target object; the detection result analysis module 302 is used to input the detection results into a preset neuroimmunological detection result analysis model to obtain the disease segment classification result corresponding to the detection result, and read the target neuroimmunological detection information according to the disease segment classification result; the detection report generation module 303 is used to generate a target neuroimmunological detection report based on the detection results, disease segment classification result and target neuroimmunological detection information, and send the target neuroimmunological detection report to the target client.

[0096] The technical solution of this embodiment obtains the test results of at least one neuroimmunological test associated with the target object, inputs the test results into a preset neuroimmunological test result analysis model, obtains the disease segment classification result corresponding to the test result, reads the target neuroimmunological test information based on the disease segment classification result, generates a target neuroimmunological test report based on the test results, disease segment classification result and target neuroimmunological test information, and sends the target neuroimmunological test report to the target client. This solves the problem of the limited content of test reports from third-party testing institutions, and realizes the generation of rich report content based on test results. It can provide medical staff and patients with detailed analysis reports and reference information related to the test results, and assist in the clinical interpretation of test results.

[0097] Optionally, the detection result analysis module 302 is also used for:

[0098] Obtain the primary interpretation results of the test results, and determine whether to perform secondary interpretation based on the primary interpretation results;

[0099] When it is determined that a secondary interpretation is required, the results of the secondary interpretation are obtained, and the target neuroimmunological detection report is updated based on the results of the secondary interpretation.

[0100] Optionally, the detection result analysis module 302 is also used for:

[0101] Determine whether the primary interpretation results correspond to the corresponding clinical symptoms; and / or,

[0102] Determine whether the antibody type in the primary interpretation results is a novel or rare antibody; and / or,

[0103] Determine if any abnormal values ​​appear in the first-level interpretation results.

[0104] Optionally, the detection result analysis module 302 is also used for:

[0105] Based on the index information of the disease course segment classification results, match the case course data associated with the test results in the preset neuroimmunological database;

[0106] Based on the disease course segment classification results, determine the node position of the test result in the classification tree corresponding to the case disease course data;

[0107] The disease course data of the target case is read based on the node location and used as the target neuroimmunological detection information.

[0108] Optionally, the detection result analysis module 302 is also used for:

[0109] Based on the keywords in the disease course segment classification results, target references are matched in a preset neuroimmune database as target neuroimmune detection information.

[0110] Optionally, the detection result analysis module 302 is also used for:

[0111] Historical neuroimmune test results and corresponding disease segment classification labels from a pre-set neuroimmune database are used as model training sample data.

[0112] The model sample data is input into the initial neuroimmunological detection result analysis model for training until the model loss function converges, thus obtaining the preset neuroimmunological detection result analysis model.

[0113] Optionally, the detection result analysis module 302 is also used for:

[0114] Generate corresponding index information for the historical neuroimmunological test results and corresponding disease segment classification results in the preset neuroimmunological database;

[0115] A target ordered multi-way tree is generated based on the disease course segments and index information. The weight values ​​of each node branch in the target ordered multi-way tree represent the correlation coefficients between disease course segments.

[0116] The neuroimmunological detection and analysis device provided in this embodiment of the invention can execute the neuroimmunological detection and analysis method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0117] Figure 4 This is a structural block diagram of a neuroimmunological detection and analysis system provided in an embodiment of the present invention. This embodiment is applicable to scenarios where third-party testing institutions perform neuroimmunological testing according to the testing requirements of hospitals and generate reports based on the test results. The system can be implemented by software and / or hardware and integrated into a server device with application development capabilities.

[0118] The system includes:

[0119] A neuroimmunological detection server 401 and at least one neuroimmunological detection client 402.

[0120] The neuroimmunological detection client 402 is used to determine the target neuroimmunological detection items for the target object based on the user's input information, and send the target neuroimmunological detection item information to the neuroimmunological detection server 401.

[0121] The neuroimmunological detection server 401 is used to obtain the detection results of the target object based on the target neuroimmunological detection project information, and to complete any neuroimmunological detection analysis method of the present invention based on the detection results, so as to provide the neuroimmunological detection analysis results to the user and the target object.

[0122] The technical solution of this embodiment determines the target neuroimmunological testing items for the target object based on the user's input information, and sends the target neuroimmunological testing item information to the neuroimmunological testing server. It then obtains the test results for the target object based on the target neuroimmunological testing item information, inputs the test results into a preset neuroimmunological testing result analysis model to obtain the disease segment classification results corresponding to the test results, and reads the target neuroimmunological testing information based on the disease segment classification results. Based on the test results, disease segment classification results, and target neuroimmunological testing information, it generates a target neuroimmunological testing report and sends the report to the target client. This solves the problem of insufficient analysis of single-result test reports from third-party testing institutions, and achieves intelligent analysis that generates rich report content based on test results. It can provide medical staff and patients with detailed analysis reports and reference information related to the test results, assisting in the clinical interpretation of test results.

[0123] The user is someone who uses the neuroimmunological detection client 402, such as a doctor.

[0124] Optionally, outpatient doctors can enter consultation information through the interactive interface of the neuroimmunological testing client 402 and send a testing request to the neuroimmunological testing server 401. The neuroimmunological testing items are preset and can be updated regularly according to business needs, laboratory personnel, and testing requirements.

[0125] Optionally, the neuroimmunological detection client 402 can be used for:

[0126] Data transmission with the neuroimmunological testing server 401 is implemented, and a search function is added to search for disease keywords. By simply entering keywords, relevant neuroimmunological testing projects can be recommended, making it convenient for users and target groups to find the right product.

[0127] Optionally, the neuroimmunological detection server 401 can be used for:

[0128] Upon receiving the testing request and the submitted specimen, the test is conducted according to the neuroimmunological testing items, and the test results of at least one neuroimmunological testing item associated with the target object are obtained.

[0129] Optionally, the neuroimmunological detection server 401 can be used for:

[0130] Obtain the first-level interpretation result obtained by performing a first-level interpretation based on the test results, determine whether the result needs to be interpreted second-level. If not, retrieve the target reference from the preset neuroimmune database as the target neuroimmune detection information; if a second-level interpretation is required, issue a second-level interpretation request, obtain the second-level interpretation result, retrieve the target reference from the preset neuroimmune database again based on the second-level interpretation result, and merge to generate the test result.

[0131] Optionally, the neuroimmunological detection server 401 can also be used for:

[0132] Data can be transmitted with WeChat official accounts or mini-programs, enabling doctors and patients to obtain target neuroimmunological test reports in a timely manner.

[0133] Optionally, the neuroimmunological detection server 401 can be used for:

[0134] All test results, disease segment classification results, and target neuroimmunological test information are acquired in real time and stored in a preset neuroimmunological database. Multi-level data centers are created to distribute and store medical test data.

[0135] Optionally, the neuroimmunological detection server 401 can be used for:

[0136] All real-time test results, disease segment classification results, and target neuroimmunological test information are automatically captured and uploaded according to the data template. Positive antibodies are compared with antibodies in the preset neuroimmunological database. If a novel antibody is found, it is defined as a rare antibody.

[0137] Optionally, the neuroimmunological detection server 401 can be used for:

[0138] Based on the test results, disease segment classification results, and target neuroimmunological test information, a target neuroimmunological test report is generated and sent to the neuroimmunological test client 402.

[0139] In this embodiment, the neuroimmunological detection client determines the target neuroimmunological detection items for the target object based on the user's input information and sends the target neuroimmunological detection item information to the neuroimmunological detection server. The neuroimmunological detection server obtains the detection results for the target object based on the target neuroimmunological detection item information and completes any neuroimmunological detection analysis method of this embodiment based on the detection results, providing neuroimmunological detection analysis results for the user and the target object. This embodiment solves the problem of insufficient result analysis by third-party testing institutions, achieving intelligent analysis of test results and providing doctors or patients with data support such as disease stage classification results, efficacy evaluation, or recurrence prediction. It also achieves efficient information exchange and business cooperation between hospitals and third-party monitoring services.

[0140] Figure 5 This is a structural block diagram of a server device provided in an embodiment of the present invention. Figure 5 A block diagram is shown of an exemplary server device 12 suitable for implementing embodiments of the present invention. Figure 5 The server device 12 shown is merely an example and should not be construed as limiting the functionality or scope of use of the embodiments of the present invention. The server device 12 can be any terminal device with computing capabilities, such as intelligent controllers and servers, mobile phones, and other terminal devices.

[0141] like Figure 5 As shown, server device 12 is presented in the form of a general-purpose computing device. The components of server device 12 may include, but are not limited to: one or more processors 16, memory 28, and bus 18 connecting different system components (including memory 28 and processor 16).

[0142] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0143] Server device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by server device 12, including volatile and non-volatile media, removable and non-removable media.

[0144] Memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Server device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 5 Not shown; usually referred to as a "hard drive"). Although Figure 5 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0145] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0146] Server device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable users to interact with server device 12, and / or with any device that enables server device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 22. Furthermore, server device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. Figure 5 As shown, network adapter 20 communicates with other modules of server device 12 via bus 18. It should be understood that, although... Figure 5 As not shown, it can be combined with server device 12 to use other hardware and / or software modules, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0147] Processor 16 executes various functional applications and data processing by running programs stored in memory 28, such as implementing a neuroimmunological detection and analysis method provided in this embodiment, the method including:

[0148] Obtain the test results of at least one neuroimmunological test associated with the target object;

[0149] The test results are input into the preset neuroimmune test result analysis model to obtain the disease segment classification results corresponding to the test results, and the target neuroimmune test information is read according to the disease segment classification results;

[0150] Based on the test results, disease segment classification results, and target neuroimmunological test information, a target neuroimmunological test report is generated and sent to the target client.

[0151] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a neuroimmunological detection and analysis method as provided in any embodiment of the present invention, including:

[0152] Obtain the test results of at least one neuroimmunological test associated with the target object;

[0153] The test results are input into the preset neuroimmune test result analysis model to obtain the disease segment classification results corresponding to the test results, and the target neuroimmune test information is read according to the disease segment classification results;

[0154] Based on the test results, disease segment classification results, and target neuroimmunological test information, a target neuroimmunological test report is generated and sent to the target client.

[0155] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can 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.

[0156] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0157] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0158] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone 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 remote computers, the remote computer can 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 it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0159] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0160] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A neuroimmunological detection and analysis method, applied to a neuroimmunological detection server, characterized in that, include: Obtain the test results of at least one neuroimmunological test associated with the target object; The detection results are input into a preset neuroimmune detection result analysis model to obtain the disease segment classification result corresponding to the detection results, and the target neuroimmune detection information is read according to the disease segment classification result; Based on the detection results, the disease segment classification results, and the target neuroimmune detection information, a target neuroimmune detection report is generated and sent to the target client. The step of reading target neuroimmunological detection information based on the disease segment classification results includes: Based on the index information of the disease course segment classification results, match the case course data associated with the detection results in the preset neuroimmune database; Based on the disease course segment classification results, determine the node position of the detection result in the classification tree corresponding to the case disease course data; The disease course data of the target case is read according to the node position and used as the target neuroimmunological detection information; The process of constructing the classification tree includes: Generate corresponding index information for the historical neuroimmunological test results and corresponding disease segment classification results in the preset neuroimmunological database; A target ordered multi-way tree is generated based on the disease segment classification results and the index information. The weight values ​​of each node branch in the target ordered multi-way tree represent the correlation coefficients between disease segments. Each node in the target ordered multi-way tree represents a disease segment. The probability of disease development of the disease segment represented by the right child node in the target ordered multi-way tree is greater than or equal to the probability of disease development of the disease segment represented by the corresponding left sibling node.

2. The method according to claim 1, characterized in that, Before inputting the detection results into a preset neuroimmunological detection result analysis model, the method includes: Obtain the first-level interpretation result of the detection result, and determine whether to perform a second-level interpretation of the detection result based on the first-level interpretation result; When it is determined that the secondary interpretation is required, the secondary interpretation result is obtained, and the target neuroimmunological detection report is updated based on the secondary interpretation result.

3. The method according to claim 2, characterized in that, The step of determining whether to perform a secondary interpretation on the detection result based on the primary interpretation result includes: Determine whether the primary interpretation results correspond to the corresponding clinical symptoms; and / or, Determine whether the antibody type in the primary interpretation results is a novel or rare antibody; and / or, Determine whether any abnormal values ​​appear in the first-level interpretation results.

4. The method according to claim 1, characterized in that, The step of reading target neuroimmunological detection information based on the disease segment classification results also includes: Based on the keywords in the disease segment classification results, target references are matched in the preset neuroimmune database and used as the target neuroimmune detection information.

5. The method according to claim 1, characterized in that, The training process of the preset neuroimmunological detection result analysis model includes: Historical neuroimmune test results and corresponding disease segment classification labels from a pre-set neuroimmune database are used as model training sample data. The model sample data is input into the initial neuroimmunological detection result analysis model for training until the model loss function converges, thus obtaining the preset neuroimmunological detection result analysis model.

6. A neuroimmunological detection and analysis device, characterized in that, include: The test result acquisition module is used to acquire the test results of at least one neuroimmunological test item associated with the target object; The detection result analysis module is used to input the detection results into a preset neuroimmune detection result analysis model to obtain the disease segment classification result corresponding to the detection result, and to read the target neuroimmune detection information according to the disease segment classification result; The test report generation module is used to generate a target neuroimmune test report based on the test results, the disease segment classification results and the target neuroimmune test information, and send the target neuroimmune test report to the target client; The detection result analysis module is further configured to match case course data associated with the detection result in a preset neuroimmunological database based on the index information of the disease course segment classification result; determine the node position of the detection result in the classification tree corresponding to the case course data based on the disease course segment classification result; and read the target case course data based on the node position as the target neuroimmunological detection information. The process of constructing the classification tree includes: Generate corresponding index information for the historical neuroimmunological test results and corresponding disease segment classification results in the preset neuroimmunological database; A target ordered multi-way tree is generated based on the disease segment classification results and the index information. The weight values ​​of each node branch in the target ordered multi-way tree represent the correlation coefficients between disease segments. Each node in the target ordered multi-way tree represents a disease segment. The probability of disease development of the disease segment represented by the right child node in the target ordered multi-way tree is greater than or equal to the probability of disease development of the disease segment represented by the corresponding left sibling node.

7. A neuroimmunological detection and analysis system, characterized in that, include: A neuroimmunological detection server and at least one neuroimmunological detection client; The neuroimmunological detection client is used to determine the target neuroimmunological detection items for the target object based on the user's input information, and send the target neuroimmunological detection item information to the neuroimmunological detection server. The neuroimmunological detection server is used to obtain the detection results of the target object based on the target neuroimmunological detection project information, and to complete the neuroimmunological detection analysis method according to any one of claims 1-5 based on the detection results, so as to provide the user and the target object with neuroimmunological detection analysis results.

8. A server device, characterized in that, The server device includes: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the neuroimmunological detection and analysis method as described in any one of claims 1-5.

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