Efficient neurology clinical diagnosis method

Abstract

The invention discloses an efficient clinical diagnosis method for the neurology department. The method comprises the following steps that 1, patients are screened, and medical history is collected; step 2, collecting a standardized biomarker; step 3, biomarker detection; 4, scoring clinical symptoms; 5, integrating data and establishing a model; and step 6, result feedback and patient management. 7, performing clinical application and continuous improvement; and 8, constructing a digital platform. According to the method, early symptoms of neurological diseases can be identified more accurately by combining rapid detection and clinical symptom scoring of biomarkers, so that misdiagnosis and missed diagnosis are reduced, the overall diagnosis efficiency is improved, personalized diagnosis and treatment suggestions can be provided for patients, more effective follow-up treatment and follow-up visit are ensured, and the diagnosis efficiency is improved. The life quality of a patient is improved, and a doctor can obtain a comprehensive evaluation result more quickly, so that more scientific and reasonable judgment is made in clinical decision making, and the feasibility and effectiveness of clinical treatment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of neurology diagnosis, and specifically provides an efficient clinical diagnosis method for neurology. Background Art

[0002] Neurology is an important branch of medicine, mainly studying and treating diseases of the central nervous system and peripheral nervous system, including related diseases of the brain, spinal cord, peripheral nerves, and muscles. There are a wide variety of diseases involved in neurology, such as stroke, epilepsy, Parkinson's disease, Alzheimer's disease, headache, neuritis, muscle diseases, etc. With the rapid development of modern medical technology, the diagnosis and treatment methods in neurology are constantly innovating to provide more accurate and efficient medical services for patients.

[0003] However, there are still significant deficiencies in the existing technology, such as:

[0004] The diagnosis of traditional neurology diseases often relies on imaging examinations and clinical symptoms, which may lead to the neglect or misdiagnosis of early symptoms. In clinical neurology, the symptoms and biomarker data of patients are often scattered, lacking effective integration and analysis. Summary of the Invention

[0005] The purpose of the present invention is to provide an efficient clinical diagnosis method for neurology to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] An efficient clinical diagnosis method for neurology includes the following steps:

[0008] Step 1: Patient screening and medical history collection;

[0009] Step 2: Standardized biomarker collection;

[0010] Step 3: Biomarker detection;

[0011] Step 4: Clinical symptom scoring;

[0012] Step 5: Data integration and model establishment;

[0013] Step 6: Result feedback and patient management;

[0014] Step 7: Clinical application and continuous improvement;

[0015] Step 8: Digital platform construction.

[0016] Preferably, in Step 1: Patient screening and medical history collection, it specifically includes:

[0017] Patient enrollment: In the neurology outpatient clinic, doctors conduct a preliminary screening of patients, paying special attention to patients with neurological symptoms, setting criteria to ensure that each patient meets the enrollment requirements;

[0018] Medical history collection: Use a standardized questionnaire tool to conduct a detailed medical history collection, including family history, past medical history, lifestyle habits, medication use, etc., ensuring that the onset time, duration, and severity of symptoms are recorded, and quantitative scoring is taken for subsequent analysis.

[0019] Preferably, step two: standardized biomarker collection specifically includes:

[0020] Sample collection: Collect blood samples from eligible patients using standardized anticoagulant tubes and collect 5 ml of blood samples;

[0021] Sample processing: Immediately centrifuge the samples after collection, separate the serum, aliquot into multiple small tubes, and store at -80 °C; each sample should be clearly labeled, recording the collection time and patient information.

[0022] Preferably, step three: biomarker detection specifically includes:

[0023] Select and optimize the detection method: Preferably use the enzyme-linked immunosorbent assay method to detect neuron-specific enolase, brain-derived neurotrophic factor, and neurofilament protein, which are related to various neurological diseases;

[0024] Perform the detection: Dilute the samples to adapt to the detection range, strictly control the reaction conditions according to the kit instructions to ensure the reliability of the data, set negative and positive controls for each experiment to ensure the accuracy of the results.

[0025] Preferably, step four: clinical symptom scoring specifically includes:

[0026] Standardized scoring tool: Use a variety of standardized scoring tools to comprehensively evaluate the cognitive and motor functions of patients;

[0027] Perform the scoring: Doctors conduct a detailed evaluation of patients according to the standardized questionnaire, record the scores of each dimension, and use a computer-assisted scoring system to reduce human error and ensure the objectivity of the scoring.

[0028] Preferably, step five: data integration and model establishment specifically includes:

[0029] Data entry: Enter all biomarker detection results and clinical symptom scoring results into the database to ensure the integrity and accuracy of the data;

[0030] Establish a diagnostic model: Use machine learning algorithms to train historical data and establish a multivariate diagnostic model to improve the predictive ability for specific neurological diseases;

[0031] Sensitivity and specificity analysis: Evaluate the sensitivity and specificity of the model through cross-validation to provide data support for clinical applications.

[0032] Preferably, step six: Result feedback and patient management specifically includes:

[0033] Generate a result report: Generate a comprehensive evaluation report based on the model analysis results, clarify the correlation between biomarker abnormalities and clinical symptoms, and provide specific clinical suggestions;

[0034] Doctor feedback and patient communication: The doctor explains to the patient based on the report results, provides a personalized treatment plan and life suggestions, arranges regular follow-up visits to monitor the disease changes, and updates the diagnostic plan according to the latest data.

[0035] Preferably, step seven: Clinical application and continuous improvement specifically includes:

[0036] Clinical verification: Conduct clinical verification of this plan for a period of time, collect feedback data, and evaluate its effectiveness and reliability;

[0037] Feedback mechanism: Establish a feedback mechanism, regularly collect the opinions of patients and doctors, and timely adjust and optimize the selection of biomarkers and symptom assessment tools;

[0038] Knowledge update: Pay attention to the latest research progress in neurology, regularly update biomarkers and assessment tools, and maintain the forefront and effectiveness of the technical solution.

[0039] Preferably, step eight: Digital platform construction specifically includes:

[0040] Establish a digital platform: Develop a comprehensive diagnostic platform that integrates biomarker detection, symptom assessment, data analysis, and result feedback functions, and provides doctors with convenient query and decision support tools;

[0041] Patient management system: Build a patient management system to realize self-assessment and data upload of patients through a mobile APP or web platform. Doctors can obtain patient data in real time to optimize follow-up management.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] 1. By combining the rapid detection of biomarkers with clinical symptom scoring, it can more accurately identify the early symptoms of neurological diseases, thereby reducing misdiagnosis and missed diagnosis situations and improving the overall diagnostic efficiency;

[0044] 2. Through data integration and comprehensive analysis, this solution can provide personalized diagnosis and treatment recommendations for patients, ensure more effective subsequent treatment and follow-up, and improve the quality of life of patients;

[0045] 3. By establishing a data analysis model, doctors can obtain comprehensive evaluation results more quickly, thus making more scientific and reasonable judgments in clinical decision-making and enhancing the feasibility and effectiveness of clinical treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic flowchart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0048] Please refer to Figure 1 , the present invention provides a technical solution:

[0049] An efficient clinical diagnosis method for neurology includes the following steps:

[0050] Step 1: Patient screening and medical history collection;

[0051] Step 2: Standardized biomarker collection;

[0052] Step 3: Biomarker detection;

[0053] Step 4: Clinical symptom scoring;

[0054] Step 5: Data integration and model establishment;

[0055] Step 6: Result feedback and patient management;

[0056] Step 7: Clinical application and continuous improvement;

[0057] Step 8: Digital platform construction.

[0058] Step 1: Patient screening and medical history collection, specifically including:

[0059] Patient enrollment: In the neurology outpatient clinic, doctors conduct a preliminary screening of patients, especially focusing on patients with neurological symptoms, and set criteria to ensure that each patient meets the enrollment requirements;

[0060] Medical history collection: Use a standardized questionnaire tool to conduct a detailed medical history collection, including family history, past medical history, lifestyle habits, medication use, etc. Ensure to record the onset time, duration, and severity of symptoms, and adopt quantitative scoring for subsequent analysis.

[0061] Patient enrollment: Set up screening criteria in the neurology outpatient clinic, clarify the recruitment targets such as age and symptom manifestations, use promotional posters and doctor recommendations to recruit patients who meet the criteria, and record the basic information of the enrolled patients, including name, age, gender, contact information, etc.;

[0062] Medical history collection: Use a standardized questionnaire such as the "Neurological Disease Symptom Questionnaire" to ask patients item by item, record family history, past medical history, and medication use, ensure the completeness of data, quantify and score the onset time, duration, and severity of the patients' symptoms from 1 to 10 points, and generate a data table.

[0063] Adopting a standardized questionnaire and scoring system makes the assessment of symptoms more objective and consistent, reduces the error of doctors' subjective judgment. Through clear screening criteria, eligible patients can be better identified, ensuring the effectiveness of enrollment. The detailed record of family history and medication use can provide more comprehensive patient health information, which is helpful for subsequent diagnosis and treatment decisions. Through quantitative scoring, the subjective feelings of patients can be converted into objective data for statistical analysis.

[0064] Step 2: Standardized biomarker collection, specifically including:

[0065] Sample collection: Collect blood samples from eligible patients, use standardized anticoagulant tubes, and collect 5 ml of blood samples;

[0066] Sample processing: Immediately centrifuge the samples after collection, separate the serum, aliquot into multiple small tubes, and store at -80°C; Each sample should be clearly labeled, and the collection time and patient information should be recorded.

[0067] Sample collection is carried out by trained nurses using aseptic techniques to collect blood samples, ensuring the quality of the samples. Select anticoagulant tubes such as EDTA tubes or heparin tubes, and collect approximately 5 ml of venous blood samples.

[0068] Sample processing is to immediately place the collected blood samples into a centrifuge, set it to 3000 rpm, centrifuge for 10 minutes. After separating the serum, transfer it to multiple clearly labeled small tubes and store at -80°C.

[0069] High-quality sample collection and processing steps ensure the accuracy of subsequent biomarker detection results, thus improving the reliability of the overall diagnosis. Through effective sample processing, the stability of biomarkers in serum is ensured, providing a solid foundation for subsequent analysis (such as ELISA tests).

[0070] Step 3: Biomarker detection, specifically including:

[0071] Select and optimize the detection method: Preferably, the enzyme-linked immunosorbent assay method is used to detect neuron-specific enolase, brain-derived neurotrophic factor, and neurofilament protein, which are related to various neurological diseases;

[0072] Implement the detection: Dilute the samples to adapt to the detection range. According to the kit instructions, strictly control the reaction conditions to ensure the reliability of the data. Set negative and positive controls for each experiment to ensure the accuracy of the results.

[0073] Select and optimize the detection method: Determine to use the enzyme-linked immunosorbent assay (ELISA) as the main detection method, and purchase high-quality kits, especially for key biomarkers such as NSE (neuron-specific enolase), BDNF (brain-derived neurotrophic factor), and NfL (neurofilament light chain).

[0074] Implement the detection: Use a standardized workflow to prepare the required reagents and samples. Dilute the samples according to the kit instructions to ensure that the dilution ratio meets the detection requirements. Carry out the reaction according to the specified experimental conditions (temperature, reaction time, termination of reaction, etc.) and record the results.

[0075] The ELISA method has higher sensitivity and specificity compared to traditional detection methods (such as Western blot or radioimmunoassay). It can detect biomarkers at low concentrations, avoid false-negative results, adopt a standardized workflow and kit, reduce variability during the experiment, ensure the repeatability and reliability of the results, and is suitable for large-scale clinical applications. Use an enzyme-labeled instrument to automatically read and analyze the data, which improves the experimental efficiency, reduces human errors, and facilitates data management and subsequent analysis of the results.

[0076] Step 4: Clinical symptom scoring, specifically including:

[0077] Standardized scoring tools: Use a variety of standardized scoring tools to comprehensively evaluate the cognitive and motor functions of patients;

[0078] Implement the scoring: The doctor conducts a detailed assessment of the patient according to the standardized questionnaire, records the scores for each dimension, and adopts a computer-aided scoring system to reduce human errors and ensure the objectivity of the scoring.

[0079] Select standardized tools suitable for the assessment of neurological diseases. For example: MMSE (Mini-Mental State Examination): used to assess cognitive function, including aspects such as orientation, memory, attention, language, etc. UPDRS (Unified Parkinson’s Disease Rating Scale): specifically for the multidimensional assessment of Parkinson's disease, including motor symptoms, non-motor symptoms, and quality of life, etc. Ensure that the selected tools have been validated, possess good reliability and validity, and are suitable for the target patient group. Print out the scoring criteria and questionnaires of these tools for convenient on-site use;

[0080] Based on the standardized questionnaire, the doctor conducts a detailed interview with the patient and scores item by item

[0081] The doctor conducts a detailed face-to-face interview with the patient, uses a standardized questionnaire to ask item by item, ensuring the consistency and accuracy of scoring. The doctor should score each indicator according to the patient's actual performance and answers to ensure objectivity in the scoring process. Use a computer-aided scoring system to enter data to ensure the accuracy and objectivity of scoring;

[0082] Operation details:

[0083] During the scoring process, use a computer-aided scoring system to enter the scoring data of each patient, reducing errors that may be caused by manual input. The system should have an automatic verification function to ensure data accuracy and generate corresponding scoring reports for subsequent analysis and comparison.

[0084] Adopting standardized assessment tools such as MMSE and UPDRS can improve the consistency of scoring and reduce the influence of scoring differences among different doctors. Using a computer-aided scoring system can quickly enter and process data, improving the efficiency of scoring, especially significantly saving time in large-scale clinical studies. The combined effect of standardized scoring tools and computer-aided systems makes the scoring process more objective, reduces the interference of subjective judgment, and improves the accuracy of results.

[0085] Step Five: Data integration and model establishment, specifically including:

[0086] Data entry: Enter all biomarker test results and clinical symptom scoring results into the database to ensure data integrity and accuracy;

[0087] Establish a diagnostic model: Use machine learning algorithms to train historical data and establish a multivariate diagnostic model to improve the predictive ability for specific neurological diseases;

[0088] Sensitivity and specificity analysis: Through cross-validation, evaluate the sensitivity and specificity of the model to provide data support for clinical applications.

[0089] Data entry: Input all biomarker test results and clinical symptom score data into the database. Use a centralized database management system (DBMS), such as MySQL or SQLite, to integrate all biomarker test results, clinical symptom scores, and patient basic information. Design the database table structure to ensure that all data fields are clear and explicit for subsequent query and analysis.

[0090] Adopt standardized data fields to ensure data consistency:

[0091] Formulate standardized data fields, including biomarker names, measurement values, scoring tools, scoring results, patient IDs, etc., to ensure that the data formats of each field are consistent. During data entry, use data validation and format checking mechanisms to avoid data inconsistencies caused by input errors.

[0092] Use statistical software (such as R or Python) for data analysis: Select appropriate statistical software (such as R, the Pandas and NumPy libraries in Python), import the integrated dataset for preliminary analysis, perform data cleaning, handle missing values and outliers to ensure the quality of the input data for subsequent analysis. Select appropriate machine learning algorithms (such as logistic regression, random forest) for model training and validation. Based on the characteristics of the data and research objectives, choose suitable machine learning algorithms for modeling. For example: Logistic regression: Suitable for binary classification problems, easy to understand and interpret. Random forest: Suitable for handling complex data and has good prediction performance. Divide the training set and test set, train the model through the training set, and verify the performance of the model on the test set.

[0093] Evaluate the sensitivity and specificity of the model through cross-validation and optimize the model parameters: Adopt the K-fold cross-validation method to ensure the stability and reliability of the model on different data subsets. Evaluate the sensitivity (true positive rate) and specificity (true negative rate) of the model, and optimize the model parameters according to the evaluation results to improve the prediction performance.

[0094] A centralized database and standardized data fields can improve the efficiency of data entry, reduce errors, and ensure data consistency and integrity. The diagnostic model constructed through machine learning algorithms (such as random forest) can handle complex non-linear relationships and provide more accurate diagnostic results, exceeding the limitations of traditional statistical methods. Through the cross-validation method, the performance of the model can be comprehensively evaluated, avoiding overfitting and ensuring the reliability of the model in practical applications.

[0095] Step 6: Result feedback and patient management, specifically including:

[0096] Result report generation: Generate a comprehensive evaluation report based on the model analysis results, clarify the correlation between biomarker abnormalities and clinical symptoms, and provide specific clinical suggestions;

[0097] Doctor feedback and patient communication: The doctor explains to the patient based on the report results, provides personalized treatment plans and lifestyle suggestions, arranges regular follow-ups, monitors the changes in the condition, and updates the diagnostic plan according to the latest data.

[0098] Generate a comprehensive evaluation report based on the analysis results, clarify the correlation between biomarker abnormalities and clinical symptoms: Use an automated report generation tool to integrate the analysis results into a comprehensive evaluation report. The report should include biomarker test results, clinical symptom scores, and their correlation analysis. Use charts and data visualization tools to present the results for easy understanding by doctors and patients. Ensure that the report content is clear and professional, easy to interpret for non-professionals, and accurately conveys important information.

[0099] The report includes specific clinical suggestions and follow-up examination suggestions: According to the evaluation results, provide personalized clinical suggestions, including possible treatment plans, lifestyle changes, and any necessary further examinations (such as imaging examinations, laboratory tests, etc.). Clarify the suggestions for subsequent follow-ups, including follow-up time, clinical symptoms to be observed, and treatment measures.

[0100] Doctor feedback and patient communication: The doctor communicates face-to-face with the patient based on the report results, explains the results and diagnostic suggestions, arranges face-to-face consultation time to ensure that the patient has sufficient time to communicate with the doctor. The doctor should explain the evaluation results, correlation, and clinical suggestions in detail. Provide necessary psychological support and education according to the specific situation of the patient to help the patient understand their condition and treatment plan.

[0101] Record the patient's feedback and adjust the treatment plan if necessary: During the communication process, the doctor should pay attention to recording the patient's feedback, including the patient's doubts, expectations, and acceptance of the suggestions. Adjust the treatment plan if necessary according to the patient's feedback to ensure that the patient's needs and medical goals are consistent.

[0102] By generating a clear and easy-to-understand evaluation report, doctors can communicate with patients more efficiently, help patients better understand their condition and treatment plan, enhance the patient's sense of participation. The specific clinical suggestions included in the report can make the treatment plan more personalized, customized according to the specific situation of the patient, improve the treatment effect. The systematic result feedback and follow-up examination suggestions help improve the management level of patients and ensure that patients can receive timely monitoring and adjustment in subsequent treatments.

[0103] Step 7: Clinical application and continuous improvement, specifically including:

[0104] Clinical validation: Conduct clinical validation of this protocol over a period of time, collect feedback data, and evaluate its effectiveness and reliability;

[0105] Implement this protocol in a selected patient population and record diagnostic results and clinical feedback: In clinical practice, select a specific patient population to implement the protocol, ensure that the sample is representative, and record the diagnostic results, treatment responses, and subjective feedback of each patient, including the degree of symptom improvement and changes in quality of life.

[0106] Collect data for subsequent analysis and evaluate the implementation effect: Regularly collect the data generated during the implementation process, including biomarker test results, clinical symptom scores, and treatment effects. Use statistical analysis methods (such as t-tests, analysis of variance, etc.) to analyze the collected data and evaluate the effectiveness and safety of this protocol in diagnosis and treatment.

[0107] Feedback mechanism: Establish a feedback mechanism, regularly collect the opinions of patients and doctors, and timely adjust and optimize the selection of biomarkers and symptom assessment tools;

[0108] Hold regular clinical discussion meetings to summarize the problems and successful experiences in protocol implementation: Regularly organize multidisciplinary team meetings to discuss the challenges and successful cases encountered during the protocol implementation, promote information exchange within the team, collect doctors' feedback, and explore the responses of different patient populations in order to summarize best practices and improvement measures;

[0109] Adjust the selection of biomarkers and symptom assessment tools based on feedback to improve the effectiveness of the protocol: Based on clinical feedback and data analysis results, evaluate the applicability of the selected biomarkers and symptom assessment tools. According to patients' feedback and clinical data, timely update and optimize the selected biomarkers and assessment tools to ensure the effectiveness and accuracy of the diagnosis and treatment protocol.

[0110] Knowledge update: Pay attention to the latest research progress in neurology, regularly update biomarkers and assessment tools, and maintain the forefront and effectiveness of the technical protocol.

[0111] Regularly review the latest literature and pay attention to the research progress related to neurology: Set a fixed time period (such as quarterly) to review the latest research results in the relevant field, especially the literature related to biomarkers and clinical symptom assessment. Pay attention to authoritative journals, academic conferences, and the latest clinical trial results to ensure an understanding of cutting-edge technologies and methods.

[0112] Update biomarkers and assessment tools based on research findings to ensure the modernization of the protocol: Regularly update the biomarkers and assessment tools used in the protocol according to the latest research findings to ensure that the protocol remains at the forefront of neurology research. Incorporate new technologies such as genomics and proteomics to continuously optimize and improve the protocol, enhancing the scientificity and effectiveness of clinical applications.

[0113] Through clinical validation and data analysis, deficiencies in the protocol can be promptly identified and corrected, making it more adaptable to the actual clinical environment and improving the treatment effect of patients. Regular clinical discussion sessions and feedback mechanisms can enhance communication and collaboration within the team, encourage experience sharing, extract best practices, and improve the overall medical level. Regularly update the literature and research findings to provide a scientific basis for protocol optimization, ensuring the close integration of the protocol with contemporary research and maintaining its modernization and forward-looking nature.

[0114] Step Eight: Construction of a digital platform, specifically including:

[0115] Establish a digital platform: Develop a comprehensive diagnostic platform that integrates biomarker detection, symptom assessment, data analysis, and result feedback functions, providing doctors with convenient query and decision support tools.

[0116] Patient management system: Build a patient management system that enables patients to conduct self-assessment and upload data through a mobile APP or web platform. Doctors can obtain patient data in real-time to optimize follow-up management.

[0117] Develop an integrated system including biomarker detection, symptom assessment, data storage, and report generation modules: Design the system architecture to ensure smooth data flow between modules, realize the automatic detection of biomarkers and the digitization of symptom assessment. Adopt modern development technologies (such as React or Vue.js for the front-end and Node.js or Django for the back-end) to ensure the high performance and scalability of the system. Integrate a data storage module and use a secure database (such as PostgreSQL or MongoDB) to ensure the security and privacy protection of patient data. Develop an automated report generation module to convert analysis results and recommendations into an easily understandable report format for reference by doctors and patients.

[0118] Ensure that the system has a user-friendly interface for easy use by doctors and patients: Conduct user research to understand the needs of doctors and patients, design the interface layout to be intuitive and easy to operate, provide multiple language options to meet the needs of different users, ensure that the interface is friendly and meets accessibility standards, and conduct user tests to collect feedback for optimizing the usability and interaction experience of the interface.

[0119] Formulate a regular maintenance plan, including system performance monitoring, data backup, and security vulnerability patching, to ensure the platform always runs securely and stably. Implement the latest network security measures, such as data encryption, user authentication, etc., to protect patient privacy and data security. Set up a feedback channel to enable doctors and patients to conveniently provide feedback on their usage experiences and suggestions. Regularly analyze user feedback, evaluate the usage frequency and satisfaction of platform functions, and adjust and optimize platform functions according to requirements.

[0120] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art will understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An efficient clinical diagnosis method for neurology, characterized by: The steps include: Step 1: Patient screening and medical history collection; Step 2: Standardized biomarker collection; Step 3: Biomarker detection; Step 4: Clinical symptom scoring; Step 5: Data integration and model building; Step 6: Result feedback and patient management; Step 7: Clinical application and continuous improvement; Step 8: Build a digital platform.

2. An efficient neurological clinical diagnosis method according to claim 1, characterized in that: The step 1: patient screening and medical history collection, specifically includes: Patient enrollment: In the neurology clinic, doctors conduct preliminary screening of patients, pay special attention to patients with neurological symptoms, set criteria, and ensure that each patient meets the enrollment requirements; Medical history collection: Use standardized questionnaire tools to collect detailed medical history, including family medical history, past medical history, lifestyle habits, medication status, etc., to ensure that the onset, duration and severity of symptoms are recorded, and quantitative scores are taken for subsequent analysis.

3. An efficient neurological clinical diagnosis method according to claim 2, characterized in that: The step 2: standardized biomarker collection, specifically includes: Sample collection: Blood samples were collected from eligible patients using standardized anticoagulant tubes to collect 5 ml of blood samples; Sample processing: After sample collection, centrifuge immediately to separate the serum, divide into multiple small tubes, and store at -80°C; each sample should be clearly labeled, and the collection time and patient information should be recorded.

4. An efficient clinical diagnosis method for neurology according to claim 3, characterized in that: The step three: biomarker detection, specifically includes: Select and optimize the detection method: Enzyme-linked immunosorbent assay is preferred to detect neuron-specific enolase, brain-derived neurotrophic factor, and neurofilament protein, which are associated with a variety of neurological diseases; Implementation of testing: dilute the sample to fit the test range, strictly control the reaction conditions according to the kit instructions to ensure the reliability of the data, set negative and positive controls for each experiment to ensure the accuracy of the results.

5. An efficient neurological clinical diagnosis method according to claim 1, characterized in that: The fourth step: clinical symptom scoring, specifically including: Standardized scoring tools: Use a variety of standardized scoring tools to comprehensively assess the patient's cognitive and motor functions; Implementation of scoring: Doctors conduct detailed assessments of patients based on standardized questionnaires, record the scores for each dimension, and use a computer-assisted scoring system to reduce human errors and ensure the objectivity of the scoring.

6. An efficient neurological clinical diagnosis method according to claim 1, characterized in that: The step 5: data integration and model building, specifically includes: Data entry: Enter all biomarker test results and clinical symptom score results into the database to ensure data completeness and accuracy; Build diagnostic models: Use machine learning algorithms to train historical data and build multivariate diagnostic models to improve the predictive power of specific neurological diseases; Sensitivity and specificity analysis: Through cross-validation, the sensitivity and specificity of the model were evaluated to provide data support for clinical application.

7. An efficient clinical diagnosis method for neurology according to claim 1, characterized in that: The step six: result feedback and patient management, specifically includes: Result report generation: Generate a comprehensive evaluation report based on the model analysis results, clarify the correlation between marker abnormalities and clinical symptoms, and provide specific clinical recommendations; Doctor feedback and patient communication: The doctor explains the report results to the patient and provides personalized treatment plans and life suggestions, arranges regular follow-up, monitors changes in the condition, and updates the diagnosis plan based on the latest data.

8. An efficient clinical diagnosis method for neurology according to claim 1, characterized in that: The step seven: clinical application and continuous improvement, specifically includes: Clinical validation: Conduct clinical validation of the program over a period of time, collect feedback data, and evaluate its effectiveness and reliability; Feedback mechanism: Establish a feedback mechanism to regularly collect opinions from patients and doctors, and timely adjust and optimize the selection of biomarkers and symptom assessment tools; Knowledge update: Pay attention to the latest research progress in neurology, regularly update biomarkers and evaluation tools, and keep the cutting-edge and effectiveness of technical solutions.

9. An efficient neurological clinical diagnosis method according to claim 1, characterized in that: The step eight: building a digital platform, specifically includes: Establish a digital platform: Develop a comprehensive diagnostic platform that integrates biomarker detection, symptom assessment, data analysis and result feedback functions to provide doctors with convenient query and decision support tools. Patient management system: Build a patient management system to enable patients to self-assess and upload data through mobile APP or web platform. Doctors can obtain patient data in real time and optimize follow-up management.

Images