Case data analysis system, method and equipment based on large model and medium

Abstract

The invention discloses a case data analysis system based on a large model, and the system comprises a data collection module, a preprocessing module, a large model training and optimization module, an analysis module, a visualization and interaction module, a security and privacy protection module, a data preprocessing module with an innovative algorithm, a large model training module with an adaptive learning rate formula, and a disease risk prediction module with an evaluation formula. System functions and performance indexes are clear, equipment and medium requirements are clear, the steps of the method are complete, and high efficiency, accuracy and safety of case data analysis are guaranteed; the case data processing efficiency and accuracy are improved, and powerful support is provided for medical decision making. A large model is accurately analyzed, visual and convenient to check, and a security module guarantees data privacy. The medical quality can be improved, the treatment scheme can be optimized, and the method has important application value.

Description

Technical Field

[0001] The present invention relates to the technical field of case data analysis systems, and in particular to a case data analysis system, method, device and medium based on a large model. Background Art

[0002] With the rapid development of medical information technology, medical institutions have accumulated a large amount of case data, which contains rich medical information and knowledge, and has great potential value for improving medical quality, optimizing treatment plans, and promoting medical research. However, traditional case data analysis methods face many challenges and it is difficult to fully explore and utilize the value of these data.

[0003] In the medical field, the diversity and complexity of case data is a prominent issue. Case data not only includes basic information about the patient, such as age, gender, medical history, etc., but also covers various aspects of information such as various symptoms, diagnostic results, examination and testing reports (such as laboratory test data, medical images, etc.), treatment process, and follow-up records. These data sources are widely sourced, involving different information systems such as electronic medical record systems, medical image storage and transmission systems, laboratory information management systems, etc. The data formats and standards are different, and there are a large number of unstructured text data, such as medical record descriptions, doctor diagnostic reports, etc., which makes data integration and analysis extremely difficult.

[0004] Traditional data analysis methods are inefficient and have limited accuracy when processing these massive case data. For disease diagnosis, it mainly relies on the doctor's personal experience and limited medical knowledge base. The lack of systematic data analysis support is likely to lead to misdiagnosis and misdiagnosis. In terms of evaluation of treatment plan effects, it is often based on small-scale clinical observations and experience summary, and it is difficult to comprehensively and accurately evaluate the pros and cons of different treatment plans, and it is impossible to provide patients with the best personalized treatment recommendations. There is also a lack of effective quantitative models and methods for predicting disease risk, making it difficult to accurately predict the probability of patients' recurrence or complications in advance, and effective preventive measures cannot be taken in a timely manner.

[0005] At the same time, the security and privacy protection of medical data are crucial issues. Case data contains sensitive information of the patient, which will cause serious damage to the patient's rights and interests once leaked or abused. However, in the storage, transmission and processing of data, there are many security risks, such as data leakage, tampering, etc. Traditional security measures often find it difficult to meet the growing security needs, and require more advanced encryption technology and strict authority management mechanisms to ensure data security and patient privacy.

[0006] With the development of artificial intelligence technology, especially deep learning technology, large models have achieved remarkable results in natural language processing, image recognition and other fields. Applying large models to case data analysis has great potential, which can automatically learn complex features and patterns in case data, achieving more accurate disease diagnosis assistance, more comprehensive treatment plan effectiveness evaluation and more reliable disease risk prediction. However, in the medical field, the systems and methods for applying large models to case data analysis are still in the development stage, and there are many technical difficulties that need to be solved, such as how to efficiently collect and preprocess diverse case data to adapt to the input requirements of large models, how to optimize the training algorithm of large models to improve the performance and accuracy of the model, how to present the analysis results of large models to doctors in an intuitive and easy-to-understand way and achieve good interaction, and how to ensure the security and privacy of the data throughout the process.

[0007] Therefore, in order to make full use of the rich case data resources of medical institutions, improve the quality and efficiency of medical services, and promote the development of medical research, the development of a large model-based case data analysis system, method, equipment and media has important practical significance and urgent needs. This innovative solution will hopefully break through the limitations of traditional case data analysis and bring new development opportunities and changes to the healthcare industry. Summary of the invention

[0008] The present invention proposes a large model-based case data analysis system, method, device and medium to solve the problems mentioned in the above-mentioned prior art.

[0009] In order to achieve the above object, the present invention adopts the following technical solution: a case data analysis system based on a large model, comprising:

[0010] The data collection module is used to collect various case data in medical institutions, including patient identity information, symptom manifestations, diagnosis results, examination and testing reports, treatment processes and follow-up records. The data sources cover electronic medical record systems, medical image storage and transmission systems, and laboratory information management systems. They have data interfaces to ensure the accuracy and complete collection of data, and the integrity of data collection reaches more than 95%;

[0011] The data preprocessing module is connected to the data acquisition module to clean, normalize and structure the collected case data, remove duplicates, errors and irrelevant data, and uniformly convert the data format to meet the input requirements of the big model. The data accuracy after data cleaning reaches more than 98%, and unstructured text data, including medical record descriptions and diagnostic reports, can be converted into structured data formats for subsequent analysis.

[0012] In the process of data preprocessing, the word embedding algorithm is used for the vectorization of text data. Suppose the text data is T = {w1, w2, ..., w n}, where w i Represents the i-th word in the text, which is converted into a vector representation using the following formula:

[0013] V={v1,v2,…,v n}

[0014] v i =f(w i ,E)+g(w i ,C)

[0015] Where f(w i , E) is a word vector mapping function based on the pre-trained word vector table E, by finding the corresponding w in E i The vector of g(w i , C) is a dynamic adjustment function based on context information C, which obtains an adjustment vector by analyzing and calculating the words in the context window. In this way, the semantic information in the text data can be captured more accurately, and the accuracy of subsequent analysis can be improved;

[0016] The large model training and optimization module uses pre-processed data to train and optimize the pre-built large model. The large model is based on the deep learning algorithm of the Transformer architecture and has a multi-layer neural network structure. It can automatically learn features and patterns in case data. The accuracy of model training reaches more than 85% on the verification set. With the continuous update of the data and the increase in the number of training times, the model can automatically adjust parameters to improve performance. The accuracy on the same verification set after each training is no less than 1%.

[0017] During the model training process, an adaptive learning rate adjustment formula is used to optimize the training process. Assume that the current training round is t, the initial learning rate is η0, and the current loss function value is L(t), then the learning rate η(t) is adjusted according to the following formula:

[0018]

[0019] Where T max is the preset maximum number of training rounds, α is an adjustment coefficient ranging from 0.5 to 1. This formula comprehensively considers the progress of training rounds and the changes in the loss function. When the loss function decreases slowly, the learning rate is appropriately reduced to more finely adjust the model parameters; when the loss function decreases rapidly, the learning rate is appropriately increased to speed up the convergence, thereby improving the training efficiency and accuracy of the model.

[0020] The data analysis module provides in-depth analysis of case data based on trained large models, including assisted disease diagnosis analysis, evaluation of treatment plan effects, and disease risk prediction functions. It can provide doctors with decision-making support information. For example, in assisted disease diagnosis analysis, the diagnostic accuracy rate of common diseases shall not be less than 80%. In terms of disease risk prediction, it can predict the probability of disease recurrence or complications in advance, and the prediction accuracy shall not be less than 75%.

[0021] The result visualization and interaction module displays the results of data analysis to users in the form of intuitive charts, graphs and reports, including disease distribution statistics charts, treatment effect comparison charts, and patient risk assessment reports. Users can filter, query and conduct in-depth analysis of data through the interactive interface. The interface response time does not exceed 2 seconds to ensure the smoothness and efficiency of user operations.

[0022] The security and privacy protection module is responsible for ensuring the security of case data and patient privacy. It uses encryption technology to store and transmit data. The security of the encryption algorithm meets industry standards to ensure that data is not leaked and tampered. At the same time, it strictly abides by medical data privacy regulations and manages data access permissions. Only authorized personnel can access and process related data. The flexibility and accuracy of permission settings meet the complex management needs of medical institutions.

[0023] Furthermore, the data preprocessing module also has a data labeling function, which can label key information in the case data and provide richer information for the training of the large model. The labeling accuracy is not less than 95%.

[0024] Furthermore, the large model training and optimization module supports distributed training and can utilize multiple servers or computing nodes for parallel training, thereby shortening the training time and improving the training efficiency. The training speed during distributed training is at least 50% higher than that of single-machine training.

[0025] Furthermore, the data analysis module has a real-time data analysis function, which can quickly analyze newly entered case data and update the analysis results in a timely manner. The response time of real-time data analysis does not exceed 1 minute.

[0026] In disease risk prediction, the following innovative risk assessment formula is used. Suppose the patient's disease risk factor set is F = {f1, f2, ..., f m}, where f m represents the mth risk factor, including age, medical history, and genetic test results. The weight corresponding to each risk factor is w i , determined through data analysis and medical expert experience, the patient's disease risk index R is calculated as follows:

[0027]

[0028] When the preset threshold is exceeded, the patient is judged to have a higher risk of disease recurrence or complications. The accuracy of disease risk prediction can be improved by continuously adjusting and optimizing the weights and thresholds of risk factors.

[0029] Furthermore, the result visualization and interaction module supports access by mobile devices, and the mobile interface is adapted to screens of different sizes, which is easy to operate and convenient for doctors to view case analysis results anytime and anywhere. The user operation satisfaction of the mobile interface is not less than 80%.

[0030] Furthermore, a method of a case data analysis system based on a large model comprises the following steps:

[0031] S1. Data collection: Collect comprehensive case data by connecting with various information systems within medical institutions to ensure the integrity and accuracy of the data. The integrity of data collection reaches more than 95%;

[0032] S2. Data preprocessing: Clean, normalize, structure and annotate the collected case data, remove invalid data, unify the data format, convert unstructured data into structured data, and annotate key information. The accuracy of the data after data cleaning should reach more than 98%, and the accuracy of the annotation should not be less than 95%;

[0033] S3. Large model training and optimization: Use preprocessed data to train and optimize the large model, use deep learning algorithms, and continuously adjust model parameters to improve the accuracy and performance of the model. The accuracy of model training reaches more than 85% on the validation set, and the accuracy on the same validation set is improved by no less than 1% after each training.

[0034] S4. Data analysis: Based on the trained large model, case data is analyzed for disease diagnosis assistance, treatment effect evaluation, and disease risk prediction to provide support for medical decision-making. In disease diagnosis assistance analysis, the diagnostic accuracy of common diseases is not less than 80%, and in disease risk prediction, the prediction accuracy is not less than 75%;

[0035] S5. Result visualization and interaction: The data analysis results are displayed to users in a visual way and interactive functions are provided. Users can query, filter and conduct in-depth analysis of data through the interface. The interface response time does not exceed 2 seconds.

[0036] S6. Security and privacy protection: During the entire data analysis process, encryption technology is used to ensure data security, and data access rights are managed in compliance with privacy regulations to ensure the confidentiality and integrity of patient data.

[0037] Furthermore, before the data collection step, a data source evaluation step is included to evaluate the data source system in the medical institution to ensure the reliability and quality of the data. The evaluation indicators include data accuracy, completeness, and consistency. Only data sources that meet the evaluation standards are subject to data collection.

[0038] The large model training and optimization step also includes a model evaluation and selection step, in which a suitable large model architecture and parameters are selected according to different business requirements and data characteristics, multiple candidate models are evaluated and compared, and the model with the best performance is selected for training and optimization;

[0039] After the data analysis step, there is also a result verification and feedback step, which compares and verifies the data analysis results with the actual medical situation, adjusts and optimizes the model and analysis method according to the verification results, and ensures that the feedback mechanism is timely within 24 hours after the results are generated;

[0040] In the result visualization and interaction step, users are supported to customize visualization charts and report formats to meet the personalized needs of different users, and the utilization rate of customized functions is not less than 60%.

[0041] Preferably, a device for a case data analysis system based on a large model comprises:

[0042] Processor, used to run the program instructions of the case data analysis system based on the large model, with a main frequency of no less than 3.0 GHz and a core number of no less than 8 cores to ensure the efficient operation and fast data processing capabilities of the system;

[0043] A memory, connected to the processor, used to store case data, program code and parameters of the large model, and analysis results, with a storage capacity of not less than 1TB, using a high-speed solid-state hard drive, and a data read and write speed of not less than 500MB / s to meet the needs of fast storage and reading of large amounts of case data;

[0044] Communication interface, used for data communication with the information system in the medical institution, supports multiple communication protocols, such as HL7 and DICOM, to ensure the stability and compatibility of data transmission, and the communication rate is not less than 1000Mbps to ensure real-time data transmission;

[0045] Display device, used to display the visual interface of case data analysis results, with a high-resolution screen with a resolution of no less than 2560×1440 to clearly display charts and report information;

[0046] Input devices, including keyboard and mouse, are convenient for users to operate and input data. The response sensitivity of the equipment meets the needs of users for efficient operation, and the operation delay does not exceed 50ms.

[0047] A computer-readable storage medium is stored with a computer program. When the program is executed by a processor, it realizes a case data analysis method based on a large model. The storage medium is a removable storage device, including a USB flash drive or a mobile hard disk, which facilitates data migration and backup, the storage capacity is not less than 512GB, the data transmission speed is not less than 200MB / s, the program code in the storage medium is optimized and compressed to reduce storage space usage, the compression ratio is not less than 50%, and the program startup time is not more than 5 seconds.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The large model-based case data analysis system, method, device and medium of the present invention have significant beneficial effects in many aspects.

[0050] In terms of data processing, through efficient data acquisition modules, case data from different systems can be widely and accurately collected, with a completeness of more than 95%, providing rich materials for comprehensive analysis. An innovative data preprocessing module uses unique formulas to convert unstructured text data into structured formats. After cleaning, the data accuracy rate reaches more than 98%, and the label accuracy rate is no less than 95%, providing high-quality data for large model training and improving analysis accuracy.

[0051] The large model training and optimization module performed well. Based on deep learning algorithms, such as the Transformer architecture, and through innovative learning rate adjustment formulas, the training accuracy reached more than 85% on the validation set and increased by no less than 1% each time, continuously optimizing model performance so that it can accurately learn the features and patterns in case data, laying the foundation for accurate analysis.

[0052] In terms of analysis functions, the data analysis module has powerful functions such as disease diagnosis assistance, treatment plan effectiveness evaluation and disease risk prediction. The accuracy rate of diagnosing common diseases is not less than 80%, and the accuracy rate of disease risk prediction is not less than 75%, providing doctors with strong decision-making support, helping to improve medical quality and efficiency, reduce misdiagnosis and missed diagnosis, and optimize treatment plans.

[0053] The result visualization and interaction module displays the results in the form of intuitive charts, etc. The interface has a fast response, supports mobile access and is adapted to different screens. The user operation satisfaction rate is no less than 80%, making it convenient for doctors to view and analyze data anytime and anywhere, improving work convenience.

[0054] The security and privacy protection module uses advanced encryption technology and strict authority management to ensure data security and patient privacy, comply with industry standards and regulatory requirements, and eliminate data security risks.

[0055] In summary, the present invention comprehensively improves the efficiency, accuracy and security of case data analysis, brings positive impetus to the development of the medical industry, helps to improve the quality of medical services, promotes the progress of medical research, and has important application value and broad market prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a schematic block diagram of a case data analysis system based on a large model proposed by the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise" and "counterclockwise" indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0059] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, and it can be the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0060] Reference Figure 1 :A case data analysis system based on a large model, comprising:

[0061] The data collection module is used to collect various case data in medical institutions, including patient identity information, symptom manifestations, diagnosis results, examination and testing reports, treatment processes and follow-up records. The data sources cover electronic medical record systems, medical image storage and transmission systems, and laboratory information management systems. They have data interfaces to ensure the accuracy and complete collection of data, and the integrity of data collection reaches more than 95%;

[0062] The data preprocessing module is connected to the data acquisition module to clean, normalize and structure the collected case data, remove duplicates, errors and irrelevant data, and uniformly convert the data format to meet the input requirements of the big model. The data accuracy after data cleaning reaches more than 98%, and unstructured text data, including medical record descriptions and diagnostic reports, can be converted into structured data formats for subsequent analysis.

[0063] In the process of data preprocessing, the word embedding algorithm is used for the vectorization of text data. Suppose the text data is T = {w1, w2, ..., w n}, where w i Represents the i-th word in the text, which is converted into a vector representation using the following formula:

[0064] V={v1,v2,…,v n}

[0065] v i =f(w i ,E)+g(w i ,C)

[0066] Where f(w i , E) is a word vector mapping function based on the pre-trained word vector table E, by finding the corresponding w in E i The vector of g(w i , C) is a dynamic adjustment function based on context information C, which obtains an adjustment vector by analyzing and calculating the words in the context window. In this way, the semantic information in the text data can be captured more accurately, and the accuracy of subsequent analysis can be improved;

[0067] The large model training and optimization module uses pre-processed data to train and optimize the pre-built large model. The large model is based on the deep learning algorithm of the Transformer architecture and has a multi-layer neural network structure. It can automatically learn features and patterns in case data. The accuracy of model training reaches more than 85% on the verification set. With the continuous update of the data and the increase in the number of training times, the model can automatically adjust parameters to improve performance. The accuracy on the same verification set after each training is no less than 1%.

[0068] During the model training process, an adaptive learning rate adjustment formula is used to optimize the training process. Assume that the current training round is t, the initial learning rate is η0, and the current loss function value is L(t), then the learning rate η(t) is adjusted according to the following formula:

[0069]

[0070] Where T max is the preset maximum number of training rounds, α is an adjustment coefficient ranging from 0.5 to 1. This formula comprehensively considers the progress of training rounds and the changes in the loss function. When the loss function decreases slowly, the learning rate is appropriately reduced to more finely adjust the model parameters; when the loss function decreases rapidly, the learning rate is appropriately increased to speed up the convergence, thereby improving the training efficiency and accuracy of the model.

[0071] The data analysis module provides in-depth analysis of case data based on trained large models, including assisted disease diagnosis analysis, evaluation of treatment plan effects, and disease risk prediction functions. It can provide doctors with decision-making support information. For example, in assisted disease diagnosis analysis, the diagnostic accuracy rate of common diseases shall not be less than 80%. In terms of disease risk prediction, it can predict the probability of disease recurrence or complications in advance, and the prediction accuracy shall not be less than 75%.

[0072] The result visualization and interaction module displays the results of data analysis to users in the form of intuitive charts, graphs and reports, including disease distribution statistics charts, treatment effect comparison charts, and patient risk assessment reports. Users can filter, query and conduct in-depth analysis of data through the interactive interface. The interface response time does not exceed 2 seconds to ensure the smoothness and efficiency of user operations.

[0073] The security and privacy protection module is responsible for ensuring the security of case data and patient privacy. It uses encryption technology to store and transmit data. The security of the encryption algorithm meets industry standards to ensure that data is not leaked and tampered. At the same time, it strictly abides by medical data privacy regulations and manages data access permissions. Only authorized personnel can access and process related data. The flexibility and accuracy of permission settings meet the complex management needs of medical institutions.

[0074] In the present invention, the data preprocessing module also has a data labeling function, which can label key information in the case data and provide richer information for the training of the large model. The labeling accuracy is not less than 95%.

[0075] In the present invention, the large model training and optimization module supports distributed training and can utilize multiple servers or computing nodes for parallel training, thereby shortening the training time and improving the training efficiency. The training speed during distributed training is at least 50% higher than that of single-machine training.

[0076] In the present invention, the data analysis module has a real-time data analysis function, can quickly analyze newly entered case data, and update the analysis results in a timely manner. The response time of real-time data analysis does not exceed 1 minute.

[0077] In disease risk prediction, the following innovative risk assessment formula is used. Suppose the patient's disease risk factor set is F = {f1, f2, ..., f m}, where f m represents the mth risk factor, including age, medical history, and genetic test results. The weight corresponding to each risk factor is w i , determined through data analysis and medical expert experience, the patient's disease risk index R is calculated as follows:

[0078]

[0079] When the preset threshold is exceeded, the patient is judged to have a higher risk of disease recurrence or complications. The accuracy of disease risk prediction can be improved by continuously adjusting and optimizing the weights and thresholds of risk factors.

[0080] In the present invention, the result visualization and interaction module supports access by mobile devices, and the mobile interface is adapted to screens of different sizes, and is easy to operate, making it convenient for doctors to view case analysis results anytime and anywhere. The user operation satisfaction of the mobile interface is not less than 80%.

[0081] In the present invention, a method of a case data analysis system based on a large model comprises the following steps:

[0082] S1. Data collection: Collect comprehensive case data by connecting with various information systems within medical institutions to ensure the integrity and accuracy of the data. The integrity of data collection reaches more than 95%;

[0083] S2. Data preprocessing: Clean, normalize, structure and annotate the collected case data, remove invalid data, unify the data format, convert unstructured data into structured data, and annotate key information. The accuracy of the data after data cleaning should reach more than 98%, and the accuracy of the annotation should not be less than 95%;

[0084] S3. Large model training and optimization: Use preprocessed data to train and optimize the large model, use deep learning algorithms, and continuously adjust model parameters to improve the accuracy and performance of the model. The accuracy of model training reaches more than 85% on the validation set, and the accuracy on the same validation set is improved by no less than 1% after each training.

[0085] S4. Data analysis: Based on the trained large model, case data is analyzed for disease diagnosis assistance, treatment effect evaluation, and disease risk prediction to provide support for medical decision-making. In disease diagnosis assistance analysis, the diagnostic accuracy of common diseases is not less than 80%, and in disease risk prediction, the prediction accuracy is not less than 75%;

[0086] S5. Result visualization and interaction: The data analysis results are displayed to users in a visual way and interactive functions are provided. Users can query, filter and conduct in-depth analysis of data through the interface. The interface response time does not exceed 2 seconds.

[0087] S6. Security and privacy protection: During the entire data analysis process, encryption technology is used to ensure data security, and data access rights are managed in compliance with privacy regulations to ensure the confidentiality and integrity of patient data.

[0088] Furthermore, before the data collection step, a data source evaluation step is included to evaluate the data source system in the medical institution to ensure the reliability and quality of the data. The evaluation indicators include data accuracy, completeness, and consistency. Only data sources that meet the evaluation standards are subject to data collection.

[0089] In the above-mentioned large model training and optimization steps, the model evaluation and selection steps are key links to ensure system performance and adaptability. The business needs in the medical field are rich and diverse, covering disease diagnosis, treatment plan planning, prognosis evaluation and other aspects, and different types of data have their own unique characteristics, such as the text complexity of electronic medical record data and the high-dimensional characteristics of medical imaging data.

[0090] In response to this situation, it is first necessary to conduct extensive research and analysis of various large-scale model architectures suitable for medical case data analysis, including but not limited to variants based on Transformer architecture, combined architectures of convolutional neural networks and recurrent neural networks, etc. For each candidate model architecture, its initial parameter settings are determined based on specific business requirements and data characteristics. For example, for disease diagnosis tasks that mainly focus on text data, more attention may be paid to the model's parameter configuration in natural language processing, such as word vector dimensions, number of heads of attention mechanisms, etc.; for tasks involving medical image analysis, more emphasis will be placed on the parameter settings of image feature extraction layers, such as convolution kernel size, number of layers, etc.

[0091] Then, a comprehensive evaluation and comparison of multiple candidate models is performed. A variety of evaluation indicators and methods are used, including but not limited to indicators commonly used in classification tasks such as accuracy, recall, and F1 values, as well as indicators used in regression tasks such as mean square error and average absolute error. At the same time, using cross-validation technology, the preprocessed data set is divided into multiple subsets, and trained and tested on different combinations to obtain more reliable evaluation results. For example, for a data set containing 1000 case data, 50% cross-validation can be used, that is, the data can be divided into 5 pieces, 4 of them are selected for training, 1 piece is tested, repeated 5 times and the average value is taken as the final evaluation result.

[0092] In addition to quantitative evaluation indicators, qualitative analysis will also be conducted. Experts in the medical field will be invited to evaluate the output results of the model and judge the rationality and usability of the model from a medical professional perspective. For example, in disease diagnosis auxiliary analysis, experts will evaluate whether the diagnostic suggestions given by the model are consistent with medical logic and clinical experience.

[0093] In the process of comprehensive evaluation to select the best performance model, the quantitative evaluation results provide objective and quantifiable data support. For each candidate model, we calculated a series of key indicators by conducting multiple experiments on the training set and the verification set, such as accuracy is used to measure the correct prediction ratio of the model in the classification task; the recall reflects the model's ability to identify positive samples; the F1 value is a balanced indicator that comprehensively considers accuracy and recall. In the regression task, the mean square error reflects the degree of average difference between the model predicted value and the real value, and the average absolute error more intuitively shows the absolute error of the predicted value. Through careful analysis of these indicators, we can clearly understand the performance of each model in different aspects.

[0094] However, relying solely on quantitative evaluation is not enough, and qualitative evaluation adds a professional medical perspective and practical application considerations to model selection. We invite a team of senior medical experts to participate in the evaluation process. Based on rich clinical experience and expertise, experts conduct in-depth review of the output results of the model. In terms of assisted analysis of disease diagnosis, experts not only pay attention to whether the diagnostic conclusions given by the model are accurate, but also analyze whether the characteristics and logic on which the model is based are in line with medical common sense. For example, for a case of suspected heart disease, if the model only makes a diagnosis based on a single electrocardiogram indicator, and ignores the patient's family history, symptom manifestations and other comprehensive factors, experts will question its rationality. In the evaluation of treatment regimen effectiveness, experts will consider whether the therapeutic factors considered by the model are comprehensive and whether the prediction of treatment effects is consistent with actual clinical experience. For disease risk prediction, experts will combine medical research and clinical practice to judge whether the model's identification and weight allocation of risk factors is reasonable.

[0095] This comprehensive way of considering quantitative and qualitative evaluation results allows us to more comprehensively and accurately select the model that best suits current business needs. But it should be emphasized that this selection process is not a one-time and for all. As the medical business continues to develop and change, new types of diseases may appear, diagnosis and treatment standards may be updated, and business needs will also change accordingly. For example, with the development of precision medicine, the need for the diagnosis and development of personalized treatment options for certain rare diseases is increasing, which requires models to more accurately identify the characteristics of these rare diseases and provide targeted recommendations. Meanwhile, new data continues to flow into the system, which may come from new cases, new testing technologies or longer-term follow-up records. New data may contain features and patterns that have not been fully considered in the past, thus affecting the performance and applicability of the model.

[0096] Therefore, we established a dynamic model evaluation and adjustment mechanism. Selected models are re-evaluated regularly (such as monthly or quarterly), new data are included in the evaluation scope, quantitative metrics are recalculated, and experts are invited to re-examine the model's performance in new business scenarios. If we find that the current model performs poorly in new business needs and data environments, or if there are new candidate models that have more performance advantages, we will adjust the model architecture or parameters in time, or even replace the model. With this continuous optimization and adjustment, we ensure that the large model architecture and parameters that are best suited to the current situation are always used to ensure that the system maintains efficient and accurate performance in a changing medical environment. After the data analysis steps are completed, the results verification and feedback steps are key links to improve the accuracy and practicality of the system. Strictly comparing and verifying data analysis results with actual medical conditions is the core measure to ensure system reliability. Actual medical conditions are a multi-dimensional concept that covers all key aspects of the patient's diagnosis and treatment process.

[0097] The final diagnosis of patients is one of the important basis for verification. This result is usually the most accurate diagnosis determined through rigorous medical procedures such as further examinations, expert consultations, etc. For example, for some difficult conditions, multiple laboratory tests may be required, such as genetic testing, pathological biopsy, etc., and the organization of multidisciplinary experts to conduct consultations, and a final diagnosis may be obtained based on opinions from all parties. We compare the systematic analysis results of disease diagnosis with these final diagnoses one by one, and calculate indicators such as the compliance rate, misdiagnosis rate and missed diagnosis rate of the diagnosis. If the system is found to have a low diagnostic accuracy on certain disease types or specific cases, we will analyze the reasons in depth, which may be due to insufficient learning of the model for certain rare symptoms or complex cases, or the insufficient sample size of relevant cases in the dataset, resulting in insufficient training of the model.

[0098] The actual effect after treatment is also a key dimension of verification. This includes the patient's symptoms improvement, such as the degree of pain relief, functional recovery, etc., and the length of recovery time. For the results of the treatment plan effect evaluation, we compared the systematic predicted treatment effect with the actual post-treatment performance of the patient. If the system predicts that a certain treatment plan will have better results, but the actual patient's symptoms do not improve significantly or the recovery time is prolonged, we will reflect on whether the model ignores certain important influencing factors when evaluating the treatment effect, such as individual differences (age, physical fitness, underlying diseases, etc.), compliance during the treatment process, etc. Through this comparison, we can find the shortcomings of the model in evaluating treatment effects and make targeted improvements.

[0099] The actual development process of the disease, especially whether there are recurrences or complications, is crucial to verify the prediction results of disease risk. We tracked the patient's disease development trajectory after treatment through long-term follow-up records. If the system predicts that patients have a lower risk of disease recurrence, but the actual patients experience recurrence or complications in the short term, we will reexamine whether the model's identification and analysis of risk factors are comprehensive. Further exploration and inclusion of more factors related to disease recurrence may be needed, such as lifestyle factors (diet, exercise, smoking and drinking, etc.), environmental factors (exposure of harmful substances in the working environment, etc.), and new discoveries in genetic factors.

[0100] A complete feedback mechanism has been established to ensure that the verification results can be applied to system improvements in a timely and effective manner. When we find that the data analysis results differ from the actual medical situation, we will immediately organize in-depth discussions between data analysts, medical experts and system developers. Based on the discussion results, corresponding improvement measures will be formulated, including adjusting the model's algorithm, optimizing the data preprocessing process, adding or adjusting feature variables, etc. At the same time, these improvement measures will be applied to the system in a timely manner and verified in subsequent data analysis. Through this continuous result verification and feedback cycle, the accuracy and practicality of the system are continuously improved, so that it can better serve medical practice, provide doctors with more reliable decision-making support, and ultimately improve patients' diagnosis and treatment effectiveness and medical quality. .

[0101] Establish a complete set of verification mechanisms, such as establishing a verification team composed of medical experts, data analysts and system developers. The team is responsible for collecting and organizing data on actual medical conditions and comparing them one by one with the data analysis results. For the results of auxiliary analysis of disease diagnosis, indicators such as diagnostic accuracy, misdiagnosis rate and missed diagnosis rate are calculated; for the results of the treatment plan effect evaluation, the degree of difference between the actual treatment effect and the predicted effect is compared; for the results of the disease risk prediction results, the compliance rate between the actual risk events and the predicted results is counted.

[0102] Based on the verification results, the models and analysis methods are adjusted and optimized in a timely manner. If the diagnostic accuracy is found, the feature extraction part of the model may be further optimized and key features related to disease diagnosis may be added; if the evaluation of the treatment plan effect is large, it will be reexamined whether the consideration of treatment factors in the data analysis algorithm is comprehensive, whether weight adjustment is required, etc. At the same time, the adjusted models and methods are reverified using new verification data to ensure the effectiveness of the improvement.

[0103] In order to ensure the timeliness of the feedback mechanism, strict time limits have been established. Feedback processing was performed within 24 hours after the result was generated. This includes prompt feedback to data analysts and model developers so that they can quickly carry out adjustment and optimization efforts. At the same time, the relevant information is fed back to medical personnel so that they can understand the current performance of the system and possible problems, so that they can make more reasonable judgments when using the results. Through this timely feedback loop, the performance of the system is continuously improved and it can better serve medical practice. In the result visualization and interaction steps, the user-defined visualization charts and report formats are supported to meet the personalized needs of different users in the analysis of medical case data. The work roles and concerns of medical personnel vary. For example, clinicians may pay more attention to the patient's specific diagnostic information and intuitive display of treatment effects in order to make rapid treatment decisions; hospital managers may focus more on macro-level data analysis such as disease distribution and resource utilization, and are used to formulate management strategies and resource allocation plans.

[0104] The system provides a wealth of visual templates and tools. Users can choose different chart types according to their needs, such as bar charts, line charts, pie charts, scatter charts, etc., to display different types of data. Users can also make custom adjustments to the appearance settings of the chart to make it meet personal visual habits and work requirements. At the same time, in terms of report format, users can freely choose the content structure of the report, including adding or deleting specific chapters and paragraphs, adjusting the arrangement order of data, etc. For example, users can create a detailed patient-centered report that includes the patient's basic information, medical history, diagnostic process, treatment plan, and effectiveness evaluation; they can also generate a concise summary report to highlight key data analysis results and conclusions.

[0105] In order to encourage users to use custom functions, the system will provide a friendly user interface and operation guide to help users quickly master customization skills through online tutorials, video demonstrations, etc. At the same time, the system will regularly collect user feedback and continuously optimize the ease of use and flexibility of custom functions. Through these measures, we ensure that the usage rate of custom functions is not less than 60%, thereby improving user satisfaction with the system and better meeting the diverse data analysis and presentation needs in the medical field.

[0106] Preferably, a device for a case data analysis system based on a large model comprises:

[0107] Processor, used to run the program instructions of the case data analysis system based on the large model, with a main frequency of no less than 3.0 GHz and a core number of no less than 8 cores to ensure the efficient operation and fast data processing capabilities of the system;

[0108] A memory, connected to the processor, used to store case data, program code and parameters of the large model, and analysis results, with a storage capacity of not less than 1TB, using a high-speed solid-state hard drive, and a data read and write speed of not less than 500MB / s to meet the needs of fast storage and reading of large amounts of case data;

[0109] Communication interface, used for data communication with the information system in the medical institution, supports multiple communication protocols, such as HL7 and DICOM, to ensure the stability and compatibility of data transmission, and the communication rate is not less than 1000Mbps to ensure real-time data transmission;

[0110] Display device, used to display the visual interface of case data analysis results, with a high-resolution screen with a resolution of no less than 2560×1440 to clearly display charts and report information;

[0111] Input devices, including keyboard and mouse, are convenient for users to operate and input data. The response sensitivity of the equipment meets the needs of users for efficient operation, and the operation delay does not exceed 50ms.

[0112] A computer-readable storage medium plays a key role in the case data analysis system based on the big model. It stores a computer program specifically used to implement the case data analysis method. When the program is executed by the processor, it can drive the entire case data analysis process to run efficiently and accurately.

[0113] The storage medium is a removable storage device, specifically including common forms such as USB flash drives or mobile hard drives. Choosing a removable storage device has significant advantages, greatly facilitating data migration and backup operations. In actual medical scenarios, medical institutions may need to share case data between different departments and departments, or transmit data to external research institutions for collaborative research, etc. The convenience of removable storage devices makes these operations easy and easy.

[0114] In terms of storage capacity, it is required to be no less than 512GB. This is to be able to store a large amount of case data and related program codes, analysis results and other information. With the continuous accumulation of medical data and the increase in analysis needs, sufficient storage capacity can ensure the long-term stable operation of the system without the need for frequent cleaning or expansion of storage space, avoiding the risk of data loss and operation interruption.

[0115] The data transmission speed is no less than 200MB / s, which is crucial to improving the read and write efficiency of data. During the case data analysis process, rapid data transmission can reduce the waiting time for program loading and data reading, and improve the system's response speed. For example, when a large amount of case data is read from the storage medium for analysis, high-speed data transmission can ensure that the data can be loaded into the large model for processing in a timely manner, thereby achieving rapid data analysis and result output, meeting the timeliness requirements of medical work.

[0116] In order to further optimize storage efficiency, the program code in the storage medium has undergone careful optimization and compression processing. By adopting advanced compression algorithms, the compression ratio is no less than 50%. This not only effectively reduces the use of storage space, so that more data and program resources can be stored in a limited storage medium space, but also improves transmission efficiency and storage stability during data transmission and storage procedures. At the same time, the program code that has been optimized and compressed will not significantly affect the system performance when running, and the program startup time is strictly controlled to no more than 5 seconds. This means that in actual use, users can quickly start case data analysis procedures and quickly enter the working state, greatly improving work efficiency, providing strong guarantees for medical personnel to obtain case analysis results in a timely manner, and helping to improve the quality and efficiency of medical services.

[0117] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can replace or change the technical solution and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A case data analysis system based on a large model, characterized in that: include: The data collection module is used to collect various case data within medical institutions, including patient identity information, symptoms, diagnosis results, examination and test reports, treatment process and follow-up records. The data sources include electronic medical record systems, medical image storage and transmission systems, and laboratory information management systems. It has a data interface to ensure accurate and complete data collection, and the integrity of data collection reaches more than 95%; The data preprocessing module is connected to the data acquisition module to clean, normalize and structure the collected case data, remove duplicate, erroneous and irrelevant data, and convert the data format to meet the input requirements of the large model. The data accuracy after data cleaning reaches more than 98%, and it can convert unstructured text data, including medical record descriptions and diagnostic reports, into a structured data format for subsequent analysis; In the process of data preprocessing, the word embedding algorithm is used for the vectorization of text data. Suppose the text data is T = {ω1, ω2, ..., ω n }, where ω i Represents the i-th word in the text, which is converted into a vector representation using the following formula: V={υ1,υ2,…,υ n } u i =f(ω i ,E)+g(ω i ,C) Where f(ω i , E) is a word vector mapping function based on the pre-trained word vector table E, by finding the corresponding ω in E i The vector of is represented by a basis vector; g(ω i , C) is a dynamic adjustment function based on context information C, which obtains an adjustment vector by analyzing and calculating the words in the context window. In this way, the semantic information in the text data can be captured more accurately, and the accuracy of subsequent analysis can be improved; The large model training and optimization module uses the preprocessed data to train and optimize the pre-built large model. The large model is based on the deep learning algorithm of the Transformer architecture and has a multi-layer neural network structure. It can automatically learn the features and patterns in the case data. The accuracy of model training reaches more than 85% on the validation set. With the continuous update of data and the increase in the number of training times, the model can automatically adjust parameters to improve performance. After each training, the accuracy on the same validation set is improved by no less than 1%; During the model training process, an adaptive learning rate adjustment formula is used to optimize the training process. Assume that the current training round is t, the initial learning rate is η0, and the current loss function value is L(t), then the learning rate η(t) is adjusted according to the following formula: Where T max is the preset maximum number of training rounds, and α is an adjustment coefficient ranging from 0.5 to 1. When the loss function decreases slowly, the learning rate is reduced to adjust the model parameters more finely; when the loss function decreases quickly, the learning rate is increased to speed up the convergence, thereby improving the training efficiency and accuracy of the model; The data analysis module conducts in-depth analysis of case data based on the trained large model, including disease diagnosis auxiliary analysis, treatment effect evaluation, and disease risk prediction functions, which can provide doctors with decision-making support information. In disease diagnosis auxiliary analysis, the diagnostic accuracy rate of common diseases is not less than 80%, and in terms of disease risk prediction, it can predict the probability of disease recurrence or complications in patients in advance, with a prediction accuracy rate of not less than 75%; The result visualization and interaction module displays the results of data analysis to users in the form of intuitive charts, graphs and reports, including disease distribution statistics charts, treatment effect comparison charts, and patient risk assessment reports. Users can filter, query and conduct in-depth analysis of data through the interactive interface. The interface response time does not exceed 2 seconds to ensure the smoothness and efficiency of user operations. The security and privacy protection module is responsible for ensuring the security of case data and patient privacy. It uses encryption technology to store and transmit data. The security of the encryption algorithm meets industry standards to ensure that the data is not leaked or tampered with.

2. The case data analysis system based on a large model according to claim 1, characterized in that: The data preprocessing module also has a data labeling function, which can label key information in the case data and provide richer information for the training of the large model. The labeling accuracy is not less than 95%.

3. The case data analysis system based on a large model according to claim 1, characterized in that: The large model training and optimization module supports distributed training, using several servers or computing nodes for parallel training, shortening the training time and improving the training efficiency. The training speed during distributed training is at least 50% higher than that of single-machine training.

4. The case data analysis system based on a large model according to claim 1, characterized in that: The data analysis module has a real-time data analysis function, which can quickly analyze the newly entered case data and update the analysis results. The response time of real-time data analysis does not exceed 1 minute; In disease risk prediction, the following innovative risk assessment formula is used. Suppose the patient's disease risk factor set is F = {f1, f2, ..., f m }, where f m Represents the mth risk factor, including age, medical history, and genetic test results. The weight corresponding to each risk factor is ω i , determined through data analysis and medical expert experience, the patient's disease risk index R is calculated as follows: When the preset threshold is exceeded, the patient is judged to have a higher risk of disease recurrence or complications. The accuracy of disease risk prediction is improved by continuously adjusting and optimizing the weights and thresholds of risk factors.

5. The case data analysis system based on a large model according to claim 1, characterized in that: The result visualization and interaction module supports access by mobile devices, and the mobile interface is adapted to screens of different sizes, easy to operate, and convenient for doctors to view case analysis results anytime and anywhere. The user operation satisfaction of the mobile interface is not less than 80%.

6. A method for applying the case data analysis system based on a large model according to any one of claims 1 to 5, characterized in that: The following steps are involved: S1. Data collection: Collect case data by connecting with various information systems within medical institutions to ensure the integrity and accuracy of the data. The integrity of data collection reaches more than 95%; S2. Data preprocessing: Clean, normalize, structure and annotate the collected case data, remove invalid data, unify the data format, convert unstructured data into structured data, and annotate key information. The accuracy of the data after data cleaning should reach more than 98%, and the accuracy of the annotation should not be less than 95%; S3. Large model training and optimization: Use preprocessed data to train and optimize the large model, use deep learning algorithms, adjust model parameters, and improve the accuracy and performance of the model. The accuracy of model training reaches more than 85% on the validation set, and the accuracy on the same validation set is improved by no less than 1% after each training. S4. Data analysis: Based on the trained large model, case data is analyzed for disease diagnosis assistance, treatment effect evaluation, and disease risk prediction to provide support for medical decision-making. In disease diagnosis assistance analysis, the diagnostic accuracy of common diseases is not less than 80%, and in disease risk prediction, the prediction accuracy is not less than 75%; S5. Result visualization and interaction: The data analysis results are displayed to users in a visual way and interactive functions are provided. Users can query, filter and conduct in-depth analysis of data through the interface. The interface response time does not exceed 2 seconds. S6. Security and privacy protection: During the entire data analysis process, encryption technology is used to ensure data security, and data access rights are managed in compliance with privacy regulations to ensure the confidentiality and integrity of patient data.

7. The case data analysis method based on a large model according to claim 6 is characterized in that: Before the data collection step, a data source evaluation step is also included to evaluate the data source system within the medical institution to ensure the reliability and quality of the data. The evaluation indicators include data accuracy, completeness, and consistency. Only data sources that meet the evaluation standards are subject to data collection; The large model training and optimization step also includes a model evaluation and selection step, in which a suitable large model architecture and parameters are selected according to different business requirements and data characteristics, multiple candidate models are evaluated and compared, and the model with the best performance is selected for training and optimization; After the data analysis step, there is also a result verification and feedback step, which compares and verifies the data analysis results with the actual medical situation, adjusts and optimizes the model and analysis method according to the verification results, and ensures that the feedback mechanism is timely within 24 hours after the results are generated; In the result visualization and interaction step, users are supported to customize visualization charts and report formats to meet the personalized needs of different users, and the utilization rate of customized functions is not less than 60%.

8. A device for implementing the case data analysis system based on a large model according to any one of claims 1 to 5, characterized in that: include: Processor, used to run the program instructions of the case data analysis system based on the large model, with a main frequency of no less than 3.0 GHz and a core number of no less than 8 cores to ensure the efficient operation and fast data processing capabilities of the system; A memory, connected to the processor, used to store case data, program code and parameters of the large model, and analysis results, with a storage capacity of not less than 1TB, using a high-speed solid-state hard drive, and a data read and write speed of not less than 500MB / s to meet the needs of fast storage and reading of large amounts of case data; Communication interface, used for data communication with the information system in the medical institution, supports multiple communication protocols, such as HL7 and DICOM, to ensure the stability and compatibility of data transmission, and the communication rate is not less than 1000Mbps to ensure real-time data transmission; Display device, used to display the visual interface of case data analysis results, with a high-resolution screen with a resolution of no less than 2560×1440 to clearly display charts and report information; Input devices, including keyboard and mouse, are convenient for users to operate and input data. The response sensitivity of the equipment meets the needs of users for efficient operation, and the operation delay does not exceed 50ms.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the case data analysis method based on a large model as described in any one of claims 6 to 7, characterized in that: The storage medium is a removable storage device, including a USB flash drive or a mobile hard disk, which is convenient for data migration and backup. The storage capacity is not less than 512GB, and the data transmission speed is not less than 200MB / s. The program code in the storage medium is optimized and compressed to reduce the storage space occupied. The compression ratio is not less than 50%, and the program startup time does not exceed 5 seconds.

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