Urticaria comprehensive data analysis and diagnosis auxiliary system

Abstract

The invention discloses an urticaria comprehensive data analysis and diagnosis auxiliary system, and belongs to the technical field of skin diseases. The urticaria comprehensive data analysis and diagnosis auxiliary system comprises a data acquisition module, a data prediction module, an image diagnosis module and a result interaction module. The urticaria diagnosis method solves the problem that an existing single diagnosis method reduces accuracy, achieves accurate diagnosis of urticaria by collecting comprehensive data of a patient and conducting preprocessing, classification, feature extraction, model construction and optimization on the comprehensive data, meanwhile, can better provide auxiliary diagnosis service for doctors in combination with recognition and comparison of lesion areas, and improves the diagnosis efficiency. And a prediction result and a judgment result are displayed to a doctor and a patient and sent to a mobile terminal of the patient at the same time, so that the doctor and the patient can know illness state changes conveniently, and a scientific basis is provided for disease prevention and treatment strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of skin diseases, and in particular to an urticaria comprehensive data analysis and diagnosis auxiliary system. Background Art

[0002] Urticaria, also known as wheal or skin edema, is a skin disease caused by mast cell activation, which leads to dilation of small blood vessels in the skin and mucous membranes and increased permeability. Currently, most of the widely used methods for skin disease diagnosis rely on extracting gross image features of the skin appearance, learning the relationship between features and diseases, and then giving a diagnosis result. However, since the causes of urticaria are generally more complicated, only through the extraction of surface pathological features, only a single pathological information of the skin disease can be obtained, which reduces the accuracy of auxiliary diagnosis of urticaria; therefore, it does not meet the existing needs. In this regard, we propose a comprehensive data analysis and diagnosis auxiliary system for urticaria. Summary of the invention

[0003] The purpose of the present invention is to provide a comprehensive data analysis and diagnosis assistance system for urticaria. By collecting the comprehensive data of patients and performing preprocessing, classification, feature extraction, model construction and optimization on the data, accurate diagnosis of urticaria is achieved. At the same time, combined with the identification and comparison of lesion areas, it may better provide auxiliary diagnosis services for doctors. The prediction results and judgment results are displayed to doctors and patients and sent to the patient's mobile terminal at the same time, so that doctors and patients can understand changes in the disease, thereby solving the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a comprehensive data analysis and diagnosis auxiliary system for urticaria, comprising: Data acquisition module, used to: Collect comprehensive data of urticaria patients through smart sensors, including medical history data, routine test data and skin imaging data; Pre-process, classify and archive the collected comprehensive data; Data prediction module, used for: Extract features from the preprocessed comprehensive data and fuse the extracted features; Based on the fused features, a prediction model based on machine learning is constructed, and the constructed prediction model is trained and optimized; Deploy the trained prediction model into practical applications to provide auxiliary diagnosis services for doctors and patients; Image diagnostic module for: A lesion recognition model is constructed based on deep learning, and the skin image data in the comprehensive data is used to train and optimize the constructed lesion recognition model; The trained model is deployed in practical applications to identify the skin image data of the patient in two periods and identify the lesion areas in the two periods, where the two periods are the patient's last treatment period and the current treatment period; Compare the areas of the two lesion areas to determine whether the patient's urticaria has improved, wherein the two lesion areas are the lesion area during the last treatment period and the lesion area during the current treatment period; Result interaction module, used to: Present the prediction results of the data prediction module and the judgment results of the image diagnosis module to doctors and patients; At the same time, the prediction results and judgment results are sent to the patient's mobile terminal through various methods.

[0005] Furthermore, the data acquisition module includes: Data collection module for: Comprehensive data of urticaria patients are collected through smart sensors, including medical records, routine test data, and skin imaging data. Routine test data include blood routine tests, allergen tests, autoantibody tests, and thyroid function tests. Data processing module for: Pre-process the collected comprehensive data, including data cleaning and data unification; Through data cleaning, incomplete, inaccurate, duplicate, damaged or irregular data in the comprehensive data are removed, which involves removing null values ​​and outliers, as well as filling missing values ​​and removing duplicates; Through data unification, comprehensive data from different sources and formats are transformed into a unified format; Among them, image enhancement is performed on the skin image data in the comprehensive data, including contrast enhancement, smoothing and edge detection; Data classification module for: The preprocessed comprehensive data are classified and numbered according to data type.

[0006] Furthermore, the contrast enhancement is performed on the skin image data in the comprehensive data, and the following steps are performed: Extracting skin image data; Performing grayscale processing on the skin image data to obtain a grayscale image corresponding to the skin image data; Extracting the grayscale value of each pixel contained in the grayscale image; Comparing the grayscale value of each pixel with a preset grayscale threshold; Extracting pixel points whose grayscale values ​​are lower than the grayscale threshold as target pixel points; Scan the target pixel points, delete the target pixel points that are not connected to other target pixel points, and obtain the filtered target pixel points; Acquire multiple target areas formed by the connection of target pixel points according to the connection conditions between the filtered target pixel points; Obtaining the grayscale coefficient value corresponding to each target area according to the grayscale value of the pixel points contained in each target area; The grayscale coefficient value corresponding to each target area is obtained by the following formula: Where B represents the grayscale coefficient value corresponding to each target area; n represents the number of pixels contained in each target area; K i Represents the gray value corresponding to the i-th pixel; K z Indicates the grayscale median value corresponding to each target area; K pi represents the grayscale average of the pixels in the target area connected to the i-th pixel; K mp K represents the grayscale average value of n pixels corresponding to each target area; fp K represents the average grayscale value of pixels on the boundary of the non-target area adjacent to the edge of the target area; b Indicates the standard deviation of the gray values ​​corresponding to n pixels; The gamma value is used to adjust the contrast of the skin image data.

[0007] Furthermore, the contrast of the skin image data is adjusted by using the gamma value, including: Extract the grayscale coefficient value corresponding to each target area; Integrating the grayscale coefficient values ​​corresponding to the target area to generate a comprehensive grayscale coefficient; The comprehensive grayscale coefficient is obtained by the following formula: Among them, B z represents the comprehensive grayscale coefficient; m represents the number of target areas; B i Indicates the grayscale coefficient value corresponding to the i-th target area; B c Indicates the standard deviation of the grayscale coefficient corresponding to the m target areas; K zb represents the standard deviation of the grayscale median value corresponding to the m target areas; P i represents the ratio between the area of ​​the i-th target region and the image area of ​​the skin image data; Retrieving an initial contrast value corresponding to the skin image data; Using the comprehensive grayscale coefficient to enhance and adjust the initial contrast value of the skin image data to obtain skin image data after contrast adjustment; The contrast value corresponding to the contrast-adjusted skin image data is obtained by the following formula: Wherein, Q represents the contrast value corresponding to the skin image data after contrast adjustment; Q 0 Indicates the initial contrast value; B z Represents the comprehensive gamma.

[0008] Furthermore, the data classification module performs the following steps: Classify the comprehensive data according to the data type, into medical records, testing, and imaging; Create corresponding folders according to the classified data types, and put the medical record data, routine test data and skin imaging data into corresponding folders respectively; Once the classification is complete, number the created folders, including the file name, patient name, and ID number.

[0009] Furthermore, the data prediction module includes: Feature extraction module for: Extract features from the preprocessed comprehensive data and fuse the extracted features; Divide the fused features into training set and test set with a ratio of 7:3 or 8:2; Model building modules for: Use machine learning to build prediction models based on the comprehensive data and fused features; Training optimization module for: Use the training set to train the constructed prediction model. During the training process, observe the performance indicators of the prediction model, including accuracy, recall rate, and F1 score, and continuously adjust the performance indicators until the prediction model reaches the best performance; After completing the training of the prediction model, use the test set to evaluate the performance of the prediction model. If the prediction model performance is poor, optimize the prediction model, including adding or deleting features and adjusting parameters. Model deployment module, used to: The trained prediction model is deployed in practical applications, and the new comprehensive data of urticaria patients is input into the prediction model for prediction to obtain the prediction results of urticaria patients, and auxiliary diagnosis services are provided to doctors and patients based on the prediction results.

[0010] Furthermore, the feature extraction module is specifically: Clustering technology or statistical methods are used to extract features from the preprocessed comprehensive data, where: Characteristics from medical record data, including medical history, onset, duration of illness, seasonality, and accompanying symptoms; Features in routine test data, including laboratory test results for blood routine, allergens, autoantibodies, and thyroid function; Features in skin imaging data, including images of skin lesions such as wheal rash and angioedema; After the feature extraction is completed, the extracted features are fused using simple averaging method and linear combination method.

[0011] Furthermore, the imaging diagnosis module includes: Model creation module for: A lesion recognition model is constructed based on deep learning, and the skin image data in the comprehensive data is used to train and optimize the constructed lesion recognition model; Model application module for: Deploy the trained model to actual applications, identify the skin image data of the patient's last treatment period and the current treatment period, and identify the lesion areas in the two periods; Region division module for: The threshold segmentation method is used to divide the lesion area of ​​the last treatment period and the lesion area of ​​the current treatment period respectively. After the division is completed, the areas of the two lesion areas are compared to determine whether the patient's urticaria has improved.

[0012] Furthermore, the model application module performs the following steps: Collect skin image data of the same part of the patient in two periods, which are divided into first image data and second image data, wherein the first image data is the skin image data during the last diagnosis and treatment, and the second image data is the skin image data during the current diagnosis and treatment; Using the trained lesion recognition model to respectively recognize the first image data and the second image data; The lesion region of the first image data and the lesion region of the second image data are determined through recognition by the lesion recognition model.

[0013] Furthermore, the result interaction module includes: Data display module, used for: The prediction results of the data prediction module and the judgment results of the image diagnosis module are presented to doctors and patients, and the export and download functions are provided; Doctors and patients understand the development of the disease through the displayed content, and the doctor formulates a follow-up treatment plan based on the displayed content; Result transmission module, used to: The prediction and judgment results are sent to the patient's mobile terminal via SMS and email.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The data acquisition module of the present invention collects comprehensive data of urticaria patients through intelligent sensors and performs preprocessing to improve the quality of the comprehensive data. The data prediction module extracts and fuses features of the preprocessed comprehensive data, and then constructs a prediction model based on machine learning and performs training and optimization, so as to provide auxiliary diagnosis services for doctors and patients through the trained prediction model. The image diagnosis module constructs a lesion recognition model based on deep learning, and uses the lesion recognition model to identify the patient's skin image data in two periods, and compares the identified lesion areas in the two periods to determine whether the patient's urticaria has improved. By combining the predictive analysis of comprehensive data with image recognition, accurate auxiliary diagnosis of urticaria is achieved, avoiding the diagnosis based on single pathological information alone, and effectively improving the accuracy of auxiliary diagnosis of urticaria. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the structure of the urticaria comprehensive data analysis and diagnosis auxiliary system of the present invention. DETAILED DESCRIPTION

[0016] 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.

[0017] In order to solve the technical problem that the existing technology can only obtain skin disease pathology information through surface pathological feature extraction, thereby reducing the accuracy of auxiliary diagnosis of urticaria, please refer to Figure 1 , this embodiment provides the following technical solutions: An urticaria comprehensive data analysis and diagnosis auxiliary system, comprising: Data acquisition module, used to: Collect comprehensive data of urticaria patients through smart sensors, including medical history data, routine test data and skin imaging data; Pre-process, classify and archive the collected comprehensive data; Data prediction module, used for: Extract features from the preprocessed comprehensive data and fuse the extracted features; Based on the fused features, a prediction model based on machine learning is constructed, and the constructed prediction model is trained and optimized; Deploy the trained prediction model into practical applications to provide auxiliary diagnosis services for doctors and patients; Image diagnostic module for: A lesion recognition model is constructed based on deep learning, and the skin image data in the comprehensive data is used to train and optimize the constructed lesion recognition model; The trained model is deployed in practical applications to identify the skin image data of the patient in two periods and identify the lesion areas in the two periods, where the two periods are the patient's last treatment period and the current treatment period; Compare the areas of the two lesion areas to determine whether the patient's urticaria has improved, wherein the two lesion areas are the lesion area during the last treatment period and the lesion area during the current treatment period; Result interaction module, used to: Present the prediction results of the data prediction module and the judgment results of the image diagnosis module to doctors and patients; At the same time, the prediction results and judgment results are sent to the patient's mobile terminal through various methods.

[0018] The technical effects of the above content are as follows: the data acquisition module collects comprehensive data of urticaria patients, such as medical record data, routine test data and skin imaging data, through intelligent sensors, and performs preprocessing and classification archiving for subsequent analysis; the data prediction module extracts and fuses features of the preprocessed comprehensive data, and builds a prediction model based on machine learning based on these features, and trains and optimizes the prediction model, so as to provide auxiliary diagnosis services for doctors and patients; the imaging diagnosis module builds a lesion recognition model based on deep learning, and uses skin imaging data for training and optimization to identify the lesion area; by identifying and comparing the lesion area between the patient's last treatment period and the current treatment period, it can be determined whether the patient's condition has improved; the result interaction module will display the results of data prediction and imaging diagnosis to doctors and patients, and send the results to the patient's mobile terminal in a variety of ways; doctors can analyze the patient's condition more comprehensively by combining predictive analysis of comprehensive data with image recognition, so as to achieve accurate auxiliary diagnosis of urticaria.

[0019] Data acquisition module, including: Data collection module for: Comprehensive data of urticaria patients are collected through smart sensors, including medical records, routine test data, and skin imaging data. Routine test data include blood routine tests, allergen tests, autoantibody tests, and thyroid function tests. Data processing module for: Pre-process the collected comprehensive data, including data cleaning and data unification; Through data cleaning, incomplete, inaccurate, duplicate, damaged or irregular data in the comprehensive data are removed, which involves removing null values ​​and outliers, as well as filling missing values ​​and removing duplicates; Through data unification, comprehensive data from different sources and formats are transformed into a unified format; Among them, image enhancement is performed on the skin image data in the comprehensive data, including contrast enhancement, smoothing and edge detection; Data classification module for: The preprocessed comprehensive data are classified and numbered according to data type.

[0020] The technical effect of the above content is: the data collection module uses intelligent sensors to collect comprehensive data of urticaria patients, including medical history information, routine test data (such as blood routine, allergen detection, autoantibody detection, thyroid function test, etc.) and skin imaging data, so as to provide more comprehensive patient medical history and physiological status information, which is conducive to subsequent model construction and data analysis. The data processing module pre-processes the collected comprehensive data, including data cleaning and data unification, which can remove unnecessary information and simplify the data format, making the comprehensive data easier to understand and use, and improving the efficiency of data processing. The data classification module can classify and number the pre-processed data according to the type of data, which helps to better manage and analyze the data, and also provides convenience for subsequent analysis and prediction.

[0021] Specifically, the contrast enhancement is performed on the skin image data in the comprehensive data by performing the following steps: Extracting skin image data; Performing grayscale processing on the skin image data to obtain a grayscale image corresponding to the skin image data; Extracting the grayscale value of each pixel contained in the grayscale image; Comparing the grayscale value of each pixel with a preset grayscale threshold; Extracting pixel points whose grayscale values ​​are lower than the grayscale threshold as target pixel points; Scan the target pixel points, delete the target pixel points that are not connected to other target pixel points, and obtain the filtered target pixel points; Acquire multiple target areas formed by the connection of target pixel points according to the connection conditions between the filtered target pixel points; Obtaining the grayscale coefficient value corresponding to each target area according to the grayscale value of the pixel points contained in each target area; The grayscale coefficient value corresponding to each target area is obtained by the following formula: Where B represents the grayscale coefficient value corresponding to each target area; n represents the number of pixels contained in each target area; K i Represents the gray value corresponding to the i-th pixel; K z Indicates the grayscale median value corresponding to each target area; K pi represents the grayscale average of the pixels in the target area connected to the i-th pixel; K mp K represents the grayscale average value of n pixels corresponding to each target area; fp K represents the average grayscale value of pixels on the boundary of the non-target area adjacent to the edge of the target area; b Indicates the standard deviation of the gray values ​​corresponding to n pixels; The gamma value is used to adjust the contrast of the skin image data.

[0022] The technical effect of the above technical solution is: by grayscale processing the skin image data, extracting, analyzing and adjusting the grayscale value, especially enhancing the pixels with low grayscale values ​​(i.e. darker areas), the visibility of the dark details in the image can be effectively improved. This is particularly important for identifying and analyzing key information such as skin lesions and vascular structures. In the technical solution, by screening the areas connected to the target pixels and calculating the grayscale coefficient value of each target area, it is helpful to more accurately identify and distinguish different characteristic areas in the image. This has an auxiliary decision-making effect for subsequent skin disease diagnosis, treatment plan formulation, etc. Using the calculated grayscale coefficient value to adjust the contrast of the skin image data can further improve the overall visual effect of the image, making the boundaries between areas of different grayscale levels clearer, thereby improving the readability and analysis efficiency of the image. In the process of extracting target pixels and target areas, by deleting isolated pixels that are not connected to other target pixels, the impact of noise on image quality can be reduced to a certain extent, and the purity and analysis accuracy of the image can be improved. The technical solution achieves the adaptability of contrast enhancement by calculating the grayscale coefficient value of each target area and adjusting the contrast of the skin image data accordingly. This means that for different types of skin image data, the scheme can provide appropriate contrast enhancement effects to a certain extent.

[0023] In summary, this technical solution effectively improves the contrast of skin image data, enhances the visibility and readability of image details through a series of sophisticated processing steps, and provides strong support for the accurate diagnosis and analysis of skin diseases.

[0024] Specifically, using the gamma value to adjust the contrast of the skin image data includes: Extract the grayscale coefficient value corresponding to each target area; Integrating the grayscale coefficient values ​​corresponding to the target area to generate a comprehensive grayscale coefficient; The comprehensive grayscale coefficient is obtained by the following formula: Among them, B z represents the comprehensive grayscale coefficient; m represents the number of target areas; B i Indicates the grayscale coefficient value corresponding to the i-th target area; B c Indicates the standard deviation of the grayscale coefficient corresponding to the m target areas; K zb represents the standard deviation of the grayscale median value corresponding to the m target areas; P i represents the ratio between the area of ​​the i-th target region and the image area of ​​the skin image data; Retrieving an initial contrast value corresponding to the skin image data; Using the comprehensive grayscale coefficient to enhance and adjust the initial contrast value of the skin image data to obtain skin image data after contrast adjustment; The contrast value corresponding to the contrast-adjusted skin image data is obtained by the following formula: Wherein, Q represents the contrast value corresponding to the skin image data after contrast adjustment; Q 0 Indicates the initial contrast value; B z Represents the comprehensive gamma.

[0025] The technical effect of the above technical solution is: by integrating the grayscale coefficient values ​​of each target area, a comprehensive grayscale coefficient is generated. This coefficient not only takes into account the grayscale characteristics of a single target area, but also comprehensively evaluates the grayscale distribution characteristics of all target areas by introducing parameters such as the grayscale coefficient standard deviation and the grayscale median standard deviation, which helps to more comprehensively reflect the overall grayscale characteristics of the skin image data. The initial contrast value of the skin image data is enhanced and adjusted using the comprehensive grayscale coefficient, thereby realizing adaptive adjustment of the contrast. This adjustment method takes into account the actual grayscale distribution of the image data and avoids the problem of over-enhancement or under-enhancement that may be caused by the traditional contrast adjustment method. Through intelligent adjustment of the contrast, the boundaries between areas of different grayscale levels in the skin image data are clearer and the details are more prominent. This helps to improve the readability and analysis efficiency of the image, and provides strong support for the accurate diagnosis of skin diseases. Through automated and intelligent processing procedures, this technical solution reduces the need for manual intervention and improves the efficiency and accuracy of the diagnostic process. Doctors can focus more on the analysis and diagnosis of image data without spending too much time on image preprocessing. Since this technical solution can significantly improve the contrast and readability of skin image data, doctors can see the detailed features of skin lesions more clearly during the diagnosis process, thereby enhancing the confidence and accuracy of the diagnosis.

[0026] In summary, this technical solution can achieve adaptive contrast adjustment by comprehensively evaluating the grayscale characteristics of the target area, significantly improving the quality and readability of skin image data, and providing strong support for the accurate diagnosis of skin diseases. At the same time, this technical solution also optimizes the diagnosis process and improves the efficiency and accuracy of diagnosis.

[0027] The data classification module performs the following steps: Classify the comprehensive data according to the data type, into medical records, testing, and imaging; Create corresponding folders according to the classified data types, and put the medical record data, routine test data and skin imaging data into corresponding folders respectively; Once the classification is complete, number the created folders, including the file name, patient name, and ID number.

[0028] The technical effect of the above content is: by classifying the comprehensive data and creating corresponding folders according to the data type, and then storing the collected data in their respective folders according to the data type they belong to, medical records, test and imaging related data can be stored separately, making search and management simpler and more efficient.

[0029] Data prediction module, including: Feature extraction module for: Extract features from the preprocessed comprehensive data and fuse the extracted features; Divide the fused features into training set and test set with a ratio of 7:3 or 8:2; Model building modules for: Use machine learning to build prediction models based on the comprehensive data and fused features; Training optimization module for: Use the training set to train the constructed prediction model. During the training process, observe the performance indicators of the prediction model, including accuracy, recall rate, and F1 score, and continuously adjust the performance indicators until the prediction model reaches the best performance; After completing the training of the prediction model, use the test set to evaluate the performance of the prediction model. If the prediction model performance is poor, optimize the prediction model, including adding or deleting features and adjusting parameters. Model deployment module, used to: The trained prediction model is deployed in practical applications, and the new comprehensive data of urticaria patients is input into the prediction model for prediction to obtain the prediction results of urticaria patients, and auxiliary diagnosis services are provided to doctors and patients based on the prediction results.

[0030] The technical effect of the above content is: the feature extraction module extracts features from the preprocessed comprehensive data and fuses the extracted features. Feature extraction and fusion are used to reduce the dimension of the data, so that the machine learning algorithm is easier to converge and can find the optimal solution more quickly. The model construction module uses machine learning methods, such as neural networks, decision trees, random forests, etc. to construct a prediction model, which can make the machine learning model fit the relationship between the input features and the target variables as much as possible, thereby realizing the prediction of the disease. The training optimization module uses the training set to train the constructed prediction model, and in the training process, the performance indicators of the prediction model, such as accuracy, recall rate and F1 score, etc., are observed, and the performance indicators are continuously adjusted until the prediction model reaches the best performance. After completing the training of the prediction model, the performance of the prediction model is evaluated using the test set. If it is found that the performance of the prediction model is not good, the prediction model needs to be optimized, such as adding or deleting features, or adjusting parameters, etc., and the performance of the prediction model is optimized through continuous adjustment. Finally, the model deployment module deploys the trained prediction model to actual applications, so that it can truly provide services for doctors and patients.

[0031] Feature extraction module, specifically: Clustering technology or statistical methods are used to extract features from the preprocessed comprehensive data, where: Characteristics from medical record data, including medical history, onset, duration of illness, seasonality, and accompanying symptoms; Features in routine test data, including laboratory test results for blood routine, allergens, autoantibodies, and thyroid function; Features in skin imaging data, including images of skin lesions such as wheal rash and angioedema; After the feature extraction is completed, the extracted features are fused using simple averaging method and linear combination method.

[0032] The technical effect of the above content is: clustering technology and statistical methods are used to extract features from the pre-processed comprehensive data. The extracted features are very important for understanding the patient's disease status. After the feature extraction is completed, the extracted features are fused using simple averaging method and linear combination method to obtain a richer feature set, which is helpful to better train and optimize the prediction model, thereby improving the prediction accuracy of urticaria.

[0033] Image diagnosis module, including: Model creation module for: A lesion recognition model is constructed based on deep learning, and the skin image data in the comprehensive data is used to train and optimize the constructed lesion recognition model; Model application module for: Deploy the trained model to actual applications, identify the skin image data of the patient's last treatment period and the current treatment period, and identify the lesion areas in the two periods. Specifically, perform the following steps: Collect skin image data of the same part of the patient in two periods, which are divided into first image data and second image data, wherein the first image data is the skin image data during the last diagnosis and treatment, and the second image data is the skin image data during the current diagnosis and treatment; Using the trained lesion recognition model to respectively recognize the first image data and the second image data; Determine the lesion area of ​​the first image data and the lesion area of ​​the second image data through recognition by the lesion recognition model; Region division module for: The threshold segmentation method is used to divide the lesion area of ​​the last treatment period and the lesion area of ​​the current treatment period respectively. After the division is completed, the areas of the two lesion areas are compared to determine whether the patient's urticaria has improved.

[0034] The technical effects of the above content are as follows: the model creation module uses deep learning technology to build a lesion recognition model, and uses the model to train and optimize the skin image data in the comprehensive data. The model application module deploys the trained model to the actual application environment, and identifies the skin image data of the patient's last treatment period and the current treatment period. By comparing the lesion areas of the patient's two skin image data, the changes in the lesion area are determined. In the area division module, the threshold segmentation method is used to divide the two lesion areas. By comparing the areas of the two lesion areas, it is determined whether the patient's urticaria has improved, which can provide strong support for the patient's treatment.

[0035] Result interaction module, including: Data display module, used for: The prediction results of the data prediction module and the judgment results of the image diagnosis module are displayed to doctors and patients, and the functions of exporting and downloading the displayed content are provided; Doctors and patients understand the development of the disease through the displayed content, and the doctor formulates a follow-up treatment plan based on the displayed content; Result transmission module, used to: The prediction and judgment results are sent to the patient's mobile terminal via SMS and email.

[0036] The technical effect of the above content is: the data display module is responsible for presenting the prediction results and judgment results generated by the data prediction module and the imaging diagnosis module to doctors and patients intuitively and clearly. In addition, the module also has data export and download functions, so that doctors and patients can view and save relevant data at any time. When doctors and patients need to understand the development of the disease, they can view the real-time updated prediction results and judgment results through the data display module. The displayed results can provide important reference for doctors to formulate subsequent treatment plans, helping doctors to grasp the changes in patients' conditions more accurately, and the result transmission module is responsible for sending the prediction results and judgment results to the patient's mobile terminal in the form of text messages and emails, which not only allows patients to check their disease progress at any time, but also allows patients to better communicate with doctors to ensure the effective implementation of treatment plans.

[0037] Working principle: The data acquisition module collects comprehensive data of urticaria patients through intelligent sensors, such as medical record data, routine test data and skin imaging data, and performs pre-processing and classification and archiving for subsequent analysis. The data prediction module builds a prediction model based on machine learning based on these features, which can provide auxiliary diagnosis services for doctors and patients. The imaging diagnosis module builds a lesion recognition model based on deep learning, which can identify the lesion area. By identifying and comparing the lesion area of ​​the patient in two periods, it can be determined whether the patient's condition has improved, which can provide strong support for the patient's treatment. Doctors can analyze the patient's condition more comprehensively by combining the predictive analysis of comprehensive data with image recognition, so as to achieve accurate auxiliary diagnosis of urticaria. The result interaction module will display the results of data prediction and imaging diagnosis to doctors and patients, and send the results to the patient's mobile terminal in various ways, so that doctors and patients can understand the development of the disease, and also provide important reference for doctors to formulate subsequent treatment plans.

[0038] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0039] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A comprehensive data analysis and diagnosis auxiliary system for urticaria, characterized in that: include: Data acquisition module, used to: Collect comprehensive data of urticaria patients through smart sensors, including medical history data, routine test data and skin imaging data; Pre-process, classify and archive the collected comprehensive data; Data prediction module, used for: Extract features from the preprocessed comprehensive data and fuse the extracted features; Based on the fused features, a prediction model based on machine learning is constructed, and the constructed prediction model is trained and optimized; Deploy the trained prediction model into practical applications to provide auxiliary diagnosis services for doctors and patients; Image diagnostic module for: A lesion recognition model is constructed based on deep learning, and the skin image data in the comprehensive data is used to train and optimize the constructed lesion recognition model; The trained model is deployed in practical applications to identify the skin image data of the patient in two periods and identify the lesion areas in the two periods, where the two periods are the patient's last treatment period and the current treatment period; Compare the areas of the two lesion areas to determine whether the patient's urticaria has improved, wherein the two lesion areas are the lesion area during the last treatment period and the lesion area during the current treatment period; Result interaction module, used to: Present the prediction results of the data prediction module and the judgment results of the image diagnosis module to doctors and patients; At the same time, the prediction results and judgment results are sent to the patient's mobile terminal through various methods.

2. The urticaria comprehensive data analysis and diagnosis auxiliary system according to claim 1, characterized in that: The data acquisition module comprises: Data collection module for: Comprehensive data of urticaria patients are collected through smart sensors, including medical records, routine test data, and skin imaging data. Routine test data include blood routine tests, allergen tests, autoantibody tests, and thyroid function tests. Data processing module for: Pre-process the collected comprehensive data, including data cleaning and data unification; Through data cleaning, incomplete, inaccurate, duplicate, damaged or irregular data in the comprehensive data are removed, which involves removing null values ​​and outliers, as well as filling missing values ​​and removing duplicates; Through data unification, comprehensive data from different sources and formats are transformed into a unified format; Among them, image enhancement is performed on the skin image data in the comprehensive data, including contrast enhancement, smoothing and edge detection; Data classification module for: The preprocessed comprehensive data are classified and numbered according to data type.

3. The urticaria comprehensive data analysis and diagnosis auxiliary system according to claim 2, characterized in that: The contrast enhancement is performed on the skin image data in the comprehensive data, and the following steps are performed: Extracting skin image data; Performing grayscale processing on the skin image data to obtain a grayscale image corresponding to the skin image data; Extracting the grayscale value of each pixel contained in the grayscale image; Comparing the grayscale value of each pixel with a preset grayscale threshold; Extracting pixel points whose grayscale values ​​are lower than the grayscale threshold as target pixel points; Scan the target pixel points, delete the target pixel points that are not connected to other target pixel points, and obtain the filtered target pixel points; Acquire multiple target areas formed by the connection of target pixel points according to the connection conditions between the filtered target pixel points; Obtaining the grayscale coefficient value corresponding to each target area according to the grayscale value of the pixel points contained in each target area; The grayscale coefficient value corresponding to each target area is obtained by the following formula: Where B represents the grayscale coefficient value corresponding to each target area; n represents the number of pixels contained in each target area; K i Represents the gray value corresponding to the i-th pixel; K z Indicates the grayscale median value corresponding to each target area; K pi represents the grayscale average of the pixels in the target area connected to the i-th pixel; K mp K represents the grayscale average value of n pixels corresponding to each target area; fp K represents the average grayscale value of pixels on the boundary of the non-target area adjacent to the edge of the target area; b Indicates the standard deviation of the gray values ​​corresponding to n pixels; The gamma value is used to adjust the contrast of the skin image data.

4. The urticaria comprehensive data analysis and diagnosis auxiliary system according to claim 3, characterized in that: Using the grayscale coefficient value to adjust the contrast of the skin image data includes: Extract the grayscale coefficient value corresponding to each target area; Integrating the grayscale coefficient values ​​corresponding to the target area to generate a comprehensive grayscale coefficient; The comprehensive grayscale coefficient is obtained by the following formula: Among them, B z represents the comprehensive grayscale coefficient; m represents the number of target areas; B i Indicates the grayscale coefficient value corresponding to the i-th target area; B c Indicates the standard deviation of the grayscale coefficient corresponding to the m target areas; K zb represents the standard deviation of the grayscale median value corresponding to the m target areas; P i represents the ratio between the area of ​​the i-th target region and the image area of ​​the skin image data; Retrieving an initial contrast value corresponding to the skin image data; Using the comprehensive grayscale coefficient to enhance and adjust the initial contrast value of the skin image data to obtain skin image data after contrast adjustment; The contrast value corresponding to the contrast-adjusted skin image data is obtained by the following formula: Where Q represents the contrast value of the skin image data after contrast adjustment; Q0 represents the initial contrast value; B z Represents the comprehensive gamma.

5. The urticaria comprehensive data analysis and diagnosis auxiliary system according to claim 2, characterized in that: The data classification module performs the following steps: Classify the comprehensive data according to the data type, into medical records, testing, and imaging; Create corresponding folders according to the classified data types, and put the medical record data, routine test data and skin imaging data into corresponding folders respectively; Once the classification is complete, number the created folders, including the file name, patient name, and ID number.

6. The urticaria comprehensive data analysis and diagnosis auxiliary system according to claim 1, characterized in that: The data prediction module comprises: Feature extraction module for: Extract features from the preprocessed comprehensive data and fuse the extracted features; Divide the fused features into training set and test set with a ratio of 7:3 or 8:2; Model building modules for: Use machine learning to build prediction models based on the comprehensive data and fused features; Training optimization module for: Use the training set to train the constructed prediction model. During the training process, observe the performance indicators of the prediction model, including accuracy, recall rate, and F1 score, and continuously adjust the performance indicators until the prediction model reaches the best performance; After completing the training of the prediction model, use the test set to evaluate the performance of the prediction model. If the prediction model performance is poor, optimize the prediction model, including adding or deleting features and adjusting parameters. Model deployment module, used to: The trained prediction model is deployed in practical applications, and the new comprehensive data of urticaria patients is input into the prediction model for prediction to obtain the prediction results of urticaria patients, and auxiliary diagnosis services are provided to doctors and patients based on the prediction results.

7. The urticaria comprehensive data analysis and diagnosis auxiliary system according to claim 6, characterized in that: The feature extraction module is specifically: Clustering technology or statistical methods are used to extract features from the preprocessed comprehensive data, where: Characteristics from medical record data, including medical history, onset, duration of illness, seasonality, and accompanying symptoms; Features in routine test data, including laboratory test results for blood routine, allergens, autoantibodies, and thyroid function; Features in skin imaging data, including images of skin lesions such as wheal rash and angioedema; After the feature extraction is completed, the extracted features are fused using simple averaging method and linear combination method.

8. The urticaria comprehensive data analysis and diagnosis auxiliary system according to claim 1, characterized in that: The imaging diagnosis module comprises: Model creation module for: A lesion recognition model is constructed based on deep learning, and the skin image data in the comprehensive data is used to train and optimize the constructed lesion recognition model; Model application module for: Deploy the trained model to actual applications, identify the skin image data of the patient's last treatment period and the current treatment period, and identify the lesion areas in the two periods; Region division module for: The threshold segmentation method is used to divide the lesion area of ​​the last treatment period and the lesion area of ​​the current treatment period respectively. After the division is completed, the areas of the two lesion areas are compared to determine whether the patient's urticaria has improved.

9. The urticaria comprehensive data analysis and diagnosis auxiliary system according to claim 8, characterized in that: The model application module performs the following steps: Collect skin image data of the same part of the patient in two periods, which are divided into first image data and second image data, wherein the first image data is the skin image data during the last diagnosis and treatment, and the second image data is the skin image data during the current diagnosis and treatment; Using the trained lesion recognition model to respectively recognize the first image data and the second image data; The lesion region of the first image data and the lesion region of the second image data are determined through recognition by the lesion recognition model.

10. The urticaria comprehensive data analysis and diagnosis auxiliary system according to claim 1, characterized in that: The result interaction module includes: Data display module, used for: The prediction results of the data prediction module and the judgment results of the image diagnosis module are presented to doctors and patients, and the export and download functions are provided; Doctors and patients understand the development of the disease through the displayed content, and the doctor formulates a follow-up treatment plan based on the displayed content; Result transmission module, used to: The prediction and judgment results are sent to the patient's mobile terminal via SMS and email.

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