Electronic health record management system

Abstract

The invention relates to the technical field of data management, in particular to an electronic health record management system which comprises an image data analysis module, a medical record information classification module, an input error detection module and a data access management module. According to the method, the retrieval efficiency of the image data is enhanced by identifying and marking the organ and tissue types in the medical image, combining the shooting date and the equipment type of the image and creating the image feature index, and the efficiency and the accuracy of medical record management are improved by combining automatic analysis and classification of the medical record text; data input errors are effectively detected and corrected through analysis and cross validation of data consistency, security management of data is enhanced through monitoring of user input behaviors and recognition of behavior modes, dynamic adjustment of data access authority is achieved through analysis and comparison of user behavior data, security and legality of data access are guaranteed, and user experience is improved. The safety of patient information is improved, and the quality and effect of medical services are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to an electronic health record management system. Background Art

[0002] The field of data management technology covers multiple aspects of data collection, storage, maintenance, processing and use. It focuses on efficient and secure data processing, ensuring data quality and integrity, and meeting compliance and accessibility requirements. It uses database management, data cleaning, data integration, data protection and data analysis, combined with machine learning algorithms to improve data processing processes and improve the accuracy of data analysis, improve the level of automation and intelligence of data management, and achieve data-driven insights and decisions. It is applied to financial services, health care, public services and education.

[0003] Among them, the electronic health record management system focuses on managing and storing patient health information, including collecting patients' medical history, drug information, treatment process, treatment results and a variety of key health indicators, and ensuring the security and privacy of patient information, enabling medical service providers to effectively track patient conditions, provide real-time and accurate access to medical records, achieve accurate diagnosis and personalized treatment plans, promote the optimal allocation of medical resources, and support medical institutions to exchange and share medical information on the premise of ensuring data security, reduce medical errors, and enhance patient satisfaction and the overall effectiveness of medical services.

[0004] Traditional electronic health record management systems lack the ability to effectively integrate and automate processing, have poor data management levels when dealing with complex and diverse medical data, are unable to accurately identify and process key information in images, resulting in inefficient medical record management and inaccurate information extraction, and are insufficient in data consistency detection and correction of input errors. They lack effective error prevention and correction measures, leading to medical errors that affect patient treatment outcomes, limiting the performance of the medical system when processing large-scale data, and affecting the quality and speed of medical decision-making. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an electronic health record management system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, an electronic health record management system includes:

[0007] The image data analysis module uses edge detection based on the patient's medical image information to identify the organ and tissue types in the image. It combines the image's shooting date and the type of shooting device to match the annotation information and retrieval tags for the medical image and generate an image feature index.

[0008] The medical record information classification module analyzes the text in the user's medical record based on the image feature index, identifies multiple keywords in the medical record, and automatically marks and classifies the medical record information according to the symptom characteristics and diagnosis results of multiple patients to generate a medical record classification result;

[0009] The input error detection module uses the medical record classification results to analyze the data consistency of information from multiple data sources in the target medical record, combines cross-validation of image annotation information and data integrity check, detects data input errors, and corrects the input data of the medical record to generate verified medical record data;

[0010] Based on the verified medical record data, the data access management module monitors the user's input behavior, identifies the target user's behavior pattern, compares it with the behavior pattern of the user's login identity, evaluates the legitimacy of the access request, adjusts the data access rights, and obtains the access control parameters.

[0011] As a further solution of the present invention, the step of acquiring the organ and tissue types in the image is specifically as follows:

[0012] Based on the patient's medical image information, the input image data is analyzed through the formula:

[0013] ;

[0014] Calculate the gradient amplitude of the image to obtain the pixel edge intensity value;

[0015] in, is the gradient amplitude of the image, Represents the gray value The partial derivative of is the gray value of the image, is the horizontal coordinate of the image, is the vertical coordinate of the image;

[0016] Based on the pixel edge intensity value, the contrast of the image is adjusted to mark the edge information in the image, thereby obtaining an edge information extraction record;

[0017] Based on the edge information extraction record, multiple organs and tissues in the image, including the heart and lungs, are identified by analyzing the combined features of the edge information, and organ and tissue types are generated.

[0018] As a further solution of the present invention, the step of obtaining the image feature index is specifically as follows:

[0019] matching annotation information for a plurality of identified tissues and organs, including a heart and a lung, according to the organ and tissue types, and generating an annotation information matching record;

[0020] Based on the matching records of the annotation information, combined with the shooting date and shooting device type of the medical image, the formula is:

[0021] ;

[0022] Calculate the index values ​​of multiple images and obtain the image index list;

[0023] in, is the image feature index value, represents the first elements, is corresponding to The weight coefficient of is the eigenvector The number of elements in , is the index of the element in the feature vector;

[0024] According to the image index list, the medical images are stored in a database, and the image index values ​​and storage paths are associated to generate image feature indexes.

[0025] As a further solution of the present invention, the steps of obtaining multiple keywords in the medical record are specifically as follows:

[0026] Based on the image feature index, preprocess the medical record text, including word segmentation, removing stop words, calculating the frequency of occurrence of each word in multiple documents, and counting the number of documents containing the target word to obtain a text word data set;

[0027] Using the text word dataset, use the formula:

[0028] ;

[0029] Calculate the text importance index of multiple words in a document;

[0030] in, Expressive words In the documentation The text importance index in , Words In the documentation The number of occurrences in Represents any word In the documentation The number of occurrences in is the index of the target vocabulary, is any word in the document, is the index of the target document, is the total number of documents in the document set, Contains words The number of documents;

[0031] According to the text importance index, multiple keywords in the medical record are identified by comparing the importance indexes of multiple words.

[0032] As a further solution of the present invention, the steps of obtaining the medical record classification results are specifically as follows:

[0033] Based on the multiple keywords in the medical records, by analyzing the medical record documents and expert evaluation, evaluating the importance of multiple keywords in multiple categories, matching weights for multiple medical record categories and keywords, and establishing a keyword weight list;

[0034] Based on the keyword weight list, by formula:

[0035] ;

[0036] Calculate the scores of the target document in multiple categories to obtain document category information;

[0037] in, For Documentation The keyword set in For keywords For classification The weight of For keywords In the documentation The text importance index in , For Documentation Categorized into categories, Used to determine the value of a variable that maximizes a given function. An index for medical record classification. For the target document, For Documentation Keywords in;

[0038] Based on the document category information, the medical records of multiple patients are classified and archived to generate medical record classification results.

[0039] As a further solution of the present invention, the steps for obtaining the verified medical record data are specifically as follows:

[0040] Based on the medical record classification results, analyzing information from multiple data sources in the target medical record, including analyzing the consistency of the diagnosis results and treatment information, identifying inconsistencies and contradictions, and generating document error identification results;

[0041] Based on the document error recognition results, cross-validating the image data with the text data of the associated medical records, including comparing the consistency of the pathological features in the text with the type information of the organs and tissues recorded in the image annotations, and generating a cross-validation result;

[0042] Based on the cross-validation results, combined with data integrity check, the formula:

[0043] ;

[0044] Calculate document accuracy;

[0045] in, is the document accuracy, is the weight coefficient of the cross-validation score, is the weight coefficient of the consistency analysis score, is the weight coefficient of the data integrity score, is the cross-validation score of the document information, is the consistency analysis score of the document information, is the completeness score of a single data field, is the total number of data fields, is the index of the data field;

[0046] Based on the document accuracy, data input errors are detected and corrected, including correcting inconsistent information and filling in missing information, to generate verified medical record data.

[0047] As a further solution of the present invention, the step of acquiring the behavior pattern of the target user is specifically as follows:

[0048] Based on the verified medical record data, monitoring the user's input behavior, including collecting the user's keyboard typing speed and mouse movement pattern data to obtain user behavior data;

[0049] According to the user behavior data, through the formula:

[0050] ;

[0051] Calculate the characteristic value of user behavior;

[0052] in, is the variability score of the target user's behavior pattern, It is The speed of each keyboard stroke, is the average keyboard typing speed, It is The speed of mouse movement. is the average mouse movement speed, is the measured number of keyboard strokes, is the number of measurements of mouse movement, is the iterative index of keyboard strokes, is the iteration index of the mouse movement;

[0053] Based on the characteristic values ​​of the user behaviors, the consistency and regularity of the user behaviors are evaluated, the stability of the user behaviors is analyzed, and the behavior patterns of the target users are identified.

[0054] As a further solution of the present invention, the step of obtaining the access control parameter is specifically as follows:

[0055] Based on the behavior pattern of the target user, extract the behavior pattern information associated with the user login identity, including keyboard tapping pattern and mouse operation habits, and obtain the login identity behavior feature data;

[0056] According to the login identity behavior characteristic data, use the formula:

[0057] ;

[0058] Calculate the consistency between the current access request behavior and the user's standard behavior pattern to obtain a behavior consistency score;

[0059] in, Indicates the consistency between the user's current behavior and historical behavior patterns. Represents the user's standard behavior pattern The behavior characteristic value of the event, Represents the first The behavior characteristic value of the event, represents the total number of behavioral events recorded in a given monitoring period, is the index of the behavior feature event;

[0060] According to the behavior consistency score, the legitimacy of the access request is evaluated in real time, and the data access rights of the operating user are adjusted, including restricting access and identity authentication, to obtain access control parameters.

[0061] Compared with the prior art, the advantages and positive effects of the present invention are:

[0062] In the present invention, by identifying and labeling the organ and tissue types in the medical images, combining the shooting date and device type of the image, creating an image feature index, the retrieval efficiency of the image data is enhanced, and the efficiency and accuracy of the medical record management are improved by combining the automatic analysis and classification of the medical record text. Through the analysis and cross-validation of data consistency, data input errors are effectively detected and corrected. The monitoring of user input behavior and the identification of behavior patterns strengthen the security management of data. By analyzing and comparing user behavior data, dynamic adjustment of data access rights is achieved to ensure the security and legality of data access, improve the security of patient information, and enhance the quality and effectiveness of medical services. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1is a system flow chart of the present invention;

[0064] Figure 2 A flow chart of the present invention for identifying organ and tissue types in an image;

[0065] Figure 3 The flowchart of obtaining the image feature index of the present invention;

[0066] Figure 4 A flowchart of extracting multiple keywords from medical records according to the present invention;

[0067] Figure 5 This is a flow chart of obtaining medical record classification results of the present invention;

[0068] Figure 6 This is a flow chart of obtaining verified medical record data of the present invention;

[0069] Figure 7 This is a flow chart of analyzing the behavior pattern of target users of the present invention;

[0070] Figure 8 This is a flow chart of adjusting access control parameters of the present invention. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0072] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0073] See also Figure 1 , an electronic health record management system comprising:

[0074] The image data analysis module uses edge detection based on the patient's medical image information to identify the organ and tissue types in the image. It combines the image's shooting date and the type of shooting device to match the annotation information and retrieval tags for the medical image and generate an image feature index.

[0075] The medical record information classification module analyzes the text in the user's medical record based on the image feature index, identifies multiple keywords in the medical record, and automatically tags and classifies the medical record information according to the symptom characteristics and diagnosis results of multiple patients to generate medical record classification results;

[0076] The input error detection module uses the medical record classification results to analyze the data consistency of information from multiple data sources in the target medical record, combines cross-validation of image annotation information and data integrity check, detects data input errors, and corrects the input data of the medical record to generate verified medical record data;

[0077] Based on the verified medical record data, the data access management module monitors the user's input behavior, identifies the target user's behavior pattern, compares it with the behavior pattern of the user's login identity, evaluates the legitimacy of the access request, adjusts data access rights, and obtains access control parameters.

[0078] The image feature index specifically includes organ and tissue type identification, date label, and equipment label. The medical record classification results include symptom marking information, diagnosis classification results, and treatment record classification information. The verified medical record data specifically refers to the revised diagnosis data, updated treatment information, and corrected symptom records. The access control parameters specifically include user behavior score, permission adjustment level, and legitimacy verification results.

[0079] See also Figure 2 , the specific steps for obtaining the organ and tissue types in the image are:

[0080] Based on the patient's medical image information, the input image data is analyzed through the formula:

[0081] ;

[0082] Calculate the gradient amplitude of the image to obtain the pixel edge intensity value;

[0083] in, is the gradient amplitude of the image, Represents the gray value The partial derivative of is the gray value of the image, is the horizontal coordinate of the image, is the vertical coordinate of the image;

[0084] formula:

[0085] ;

[0086] Parameter meaning and acquisition method:

[0087] : The grayscale value of the image, obtained by converting the original color image into a grayscale image;

[0088] and : are the grayscale gradients of the image in the horizontal and vertical directions, respectively, which are calculated by the grayscale value difference of adjacent pixels in the image, where , ;

[0089] and : The pixel coordinates of the image, directly obtain the position of each pixel through the pixel traversal method in the image processing software or programming library;

[0090] Calculation example:

[0091] Set the position of the target pixel in the grayscale image to (x, y) = (100, 100), the grayscale value , the gray values ​​of adjacent pixels are , , , ;

[0092] Compute the gradient:

[0093] ;

[0094] ;

[0095] Calculate the gradient magnitude :

[0096] ;

[0097] The calculation results show that the edge strength of the image at the (100,100) position is 11.18, and the value reflects the edge clarity of the image content at this position. The calculation process is used to identify and analyze the edge information in the image, providing a data basis for the edge enhancement and feature extraction steps.

[0098] Based on the pixel edge intensity value, the contrast of the image is adjusted to mark the edge information in the image and obtain the edge information extraction record;

[0099] A local histogram equalization method is used, which involves the redistribution of grayscale values ​​in the area around each pixel to enhance the local contrast of the image. During implementation, a 3x3 neighborhood window is selected, the grayscale histogram of pixels in the neighborhood is calculated, and the grayscale value of the target pixel is remapped. The process is iterative for the entire image. The adjusted image shows the edge of the organ. The edge detection algorithm Canny is used for image edge detection, and double thresholds are used to determine potential edges. The strong edge and weak edge are determined by setting high and low thresholds. The strong edge is used as the final edge, and the weak edge is retained when it is connected to the strong edge. The edge information extraction record obtained includes edge strength and edge position. The target data is used for subsequent organ and tissue type identification.

[0100] Extract records based on edge information, identify multiple organs and tissues in the image, including heart and lungs, and generate organ and tissue types by analyzing the combined features of edge information;

[0101] Support vector machine is used to classify organs and tissues in images. The feature vector of the organ is extracted through image preprocessing steps. The features include edge density, edge direction and edge strength. The target features are input into the pre-trained SVM model. The model training is based on batch labeled medical image data to distinguish different organs and tissue types such as heart, lungs, etc. Through the classification decision of the SVM model, various organs and tissue types in the image are identified. Each image area is labeled as a specific organ type according to the output of SVM. The process ensures the accuracy of image recognition and generates organ and tissue type labels.

[0102] See also Figure 3 , the steps to obtain the image feature index are as follows:

[0103] Matching annotation information for multiple identified tissues and organs, including heart and lungs, based on organ and tissue types, and generating annotation information matching records;

[0104] The annotation propagation algorithm is applied to automatically match newly identified organs and tissues to corresponding annotation categories based on existing annotation templates. The steps include loading the feature vectors of the organ and tissue recognition results, such as edge strength and shape descriptors, and comparing the target features with the templates in the annotation database. The feature similarity is calculated in each matching process, and the template with the highest similarity is selected for annotation. A specific annotation information matching record is generated, which lists the annotation information corresponding to each organ and tissue, and records the confidence of the match, providing a basis for subsequent review or adjustment.

[0105] Matching records based on annotation information, combined with the shooting date and shooting device type of the medical image, through the formula:

[0106] ;

[0107] Calculate the index values ​​of multiple images and obtain the image index list;

[0108] in, is the image feature index value, represents the first elements, including the organ and tissue type code, the shooting date code, and the device type code. is corresponding to The weight coefficient of is the eigenvector The number of elements in , is the index of the element in the feature vector;

[0109] formula:

[0110] ;

[0111] Parameter meaning and acquisition method:

[0112] : Image feature index value, used to identify and retrieve images;

[0113] : No. Numerical codes for each feature, including organ or tissue type, imaging date, and device type;

[0114] : No. The weight of each feature;

[0115] : The total number of elements in the feature vector;

[0116] Calculation example:

[0117] The target image is set to be an ultrasound medical image of the heart, and the shooting date is January 1, 2023. , , , , , , ,calculate :

[0118] ;

[0119] ;

[0120] Calculation results is the index value of the target image, ensuring that the image is effectively identified and retrieved through the index value, thereby optimizing the retrieval efficiency.

[0121] According to the image index list, the medical images are stored in the database, and the image index value and the storage path are associated to generate the image feature index;

[0122] Using the insert and update operations of the SQL database, the characteristic index values ​​of each medical image, such as the organ type code, shooting date code, and device type code, are associated with the image file. By writing SQL commands, the target information is batch inserted into the medical image database. During the process, the storage path of each image file is also recorded in the database to ensure that any specific medical image can be quickly retrieved through the index value. After the operation is completed, an operation report is generated, including the number of images successfully stored and any errors or abnormalities, for subsequent maintenance or investigation. The process ensures the integrity and traceability of the data, providing strong support for the management and use of medical images.

[0123] See also Figure 4 ,The specific steps for obtaining multiple keywords in the medical records are:

[0124] Based on the image feature index, the medical record text is preprocessed, including word segmentation, stop word removal, calculation of the frequency of each word in multiple documents, and counting of the number of documents containing the target word to obtain a text word dataset;

[0125] The word segmentation and stop word removal operations in natural language processing technology are applied to ensure that the text data is suitable for subsequent analysis. The NLTK library in the Python programming language is used to provide a wide range of text processing functions. In the word segmentation step, the NLTK word segmenter decomposes the text string into separate vocabulary elements. The English stop word list provided by NLTK is used to remove common but low-information words from the word segmentation results. The TF-IDF algorithm is used to calculate the frequency of occurrence of each word in multiple documents and the total number of documents containing the word. In the process, the TF-IDF value of the word is calculated and stored to obtain a text word dataset. The dataset records in detail the statistical information of each word and its different documents.

[0126] Using the text word dataset, use the formula:

[0127] ;

[0128] Calculate the text importance index of multiple words in a document;

[0129] in, Expressive words In the documentation The text importance index in , Words In the documentation The number of occurrences in Represents any word In the documentation The number of occurrences in is the index of the target vocabulary, Any word in the document, used to count the documents The number of occurrences of all words in , is the index of the target document, is the total number of documents in the document set, Contains words The number of documents;

[0130] formula:

[0131] ;

[0132] Parameter meaning and acquisition method:

[0133] :For words In the documentation The number of occurrences in the document Direct word frequency statistics are obtained;

[0134] : For documents The total number of occurrences of all words in the document is obtained by adding up the frequency of occurrences of all words in the document;

[0135] : is the total number of documents in the document set, obtained from the document management system database;

[0136] :Contains words The number of documents is obtained by querying the database;

[0137] Calculation example:

[0138] set up , , in the document Chinese Appear times, total number of words in the document ,calculate :

[0139] ;

[0140] ;

[0141] ;

[0142] Calculation results Indicates the word In the documentation The importance of the text in quantifies the uniqueness and information load of the word in this document compared to other documents.

[0143] According to the text importance index, multiple keywords in medical records are identified by comparing the importance index of multiple words;

[0144] The importance of each word in the text is determined based on its TF-IDF value. The TF-IDF threshold of each word is determined in the process. The threshold is determined based on statistical analysis and is usually set to the average TF-IDF value of all words. Words with TF-IDF values ​​higher than this threshold are considered keywords. This is performed using the Scikit-learn library in Python. After the TF-IDF value of each word is calculated, the word with the highest TF-IDF value is selected as the keyword. The target keyword represents the main content and theme of the document. The process ensures that important words related to specific symptoms, treatments or medical conditions are effectively identified from a large amount of medical record text.

[0145] See also Figure 5 , the specific steps for obtaining the medical record classification results are:

[0146] Based on multiple keywords in medical records, by analyzing medical record documents and expert evaluation, the importance of multiple keywords in multiple categories is evaluated, and weights are matched for multiple medical record categories and keywords to establish a keyword weight list;

[0147] A combination of automated text analysis techniques and manual evaluation is used, including machine learning-based text classification algorithms, such as support vector machines and decision trees. The target algorithm can automatically identify the correlation between keywords and categories when processing text data classification. In the process, keywords are extracted from medical record documents, and the keyword importance feedback provided by the expert group is used to adjust the initial weight of each keyword. The keyword weight is adjusted based on the correlation between the keyword and the medical record classification using the algorithm model to ensure that the weight reflects the actual importance of the keyword in the medical record classification. The weight adjustment process includes multiple iterations, and each iteration adjusts the keyword weight based on expert feedback and algorithm output to generate a keyword weight list, which records the weight of each keyword corresponding to different medical record classifications.

[0148] Based on the keyword weight list, through the formula:

[0149] ;

[0150] Calculate the scores of the target document in multiple categories to obtain document category information;

[0151] in, For Documentation The keyword set in For keywords For classification The weight of For keywords In the documentation The text importance index in , For Documentation Categorized into categories, Used to determine the value of a variable that maximizes a given function. An index for medical record classification. For the target document, For Documentation Keywords in;

[0152] formula:

[0153] ;

[0154] Parameter meaning and acquisition method:

[0155] :For keywords In category The weight in reflects the importance of the word in a specific category;

[0156] :For keywords In the documentation The text importance index in is calculated by performing text analysis and word frequency statistics on the document;

[0157] : is the document The set of all keywords in ;

[0158] : It is all possible medical record classifications, including cardiology and neurology;

[0159] Calculation example:

[0160] There are three categories , On behalf of cardiology, Represents neurology, Represents digestive system diseases, documents Contains keyword collection , For treatment, For pain, For the heart, set , , , ;

[0161] Calculate the score for each category:

[0162] Cardiology Score:

[0163] ;

[0164] Neurology Score:

[0165] ;

[0166] Digestive system diseases Score:

[0167] ;

[0168] By quantifying the relevance of each classification to the document, the classification result of the target document is obtained. The process is used for document classification in automated document management.

[0169] Based on the document category information, the medical records of multiple patients are classified and archived to generate medical record classification results;

[0170] A multi-classification logistic regression model based on keyword weights is used to handle multi-label classification problems. It is suitable for medical document classification. During the process, a classification score is calculated for each medical record document. The score is based on the weighted sum of all keywords in the document. The weight comes from the previously established keyword weight list. By setting the classification threshold, when the score of each medical record document is higher than a certain threshold, the document is classified into the corresponding medical record category. The classification process is automatic, and the classification label of each patient's medical record is updated in the database through programming. After the classification is completed, the system automatically stores the classification results and generates a medical record classification report. The results include the number of each type of medical record and the classification accuracy evaluation results, which provides medical institutions with a detailed overview of the medical record classification status and helps further medical decision-making and medical record management.

[0171] See also Figure 6 ,The specific steps for obtaining the verified medical record data are:

[0172] Based on the medical record classification results, analyze the information of multiple data sources in the target medical record, including analyzing the consistency of diagnosis results and treatment information, identifying inconsistencies and contradictions, and generating document error identification results;

[0173] Data consistency analysis tools, such as data comparison algorithms, are used to ensure matching of diagnosis and treatment information, including extracting diagnosis and treatment records from the medical record database, executing them in a Python environment through programming scripts, and checking the matching of each diagnosis record with the relevant treatment recommendations. Analysis tools check the correlation between the two types of information and identify mismatched or contradictory data points, including recording the target information as a potential error if the diagnosis result is inconsistent with the treatment recommendation. The analysis ensures a high degree of consistency between the diagnosis data and the treatment recommendations, generates document error identification results, and lists in detail all identified inconsistencies and contradictions.

[0174] Based on the document error recognition results, the image data is cross-validated with the text data of the associated medical records, including comparing the consistency of the pathological features in the text and the type information of organs and tissues recorded in the image annotations to generate cross-validation results;

[0175] Use image processing algorithms and text comparison algorithms to extract medical images and their annotation information related to medical records from the database, and obtain the pathological feature descriptions in the medical record text. Use image processing software to analyze the organ and tissue types in the image, and compare the target information with the pathological features in the text. The comparison algorithm analyzes the consistency of the two data sources and identifies inconsistent or erroneous information points. This step ensures the matching degree of image data and text data, enhances the accuracy and reliability of medical record data, generates cross-validation results, and records all data inconsistencies found.

[0176] Based on the cross-validation results, combined with data integrity check, the formula is:

[0177] ;

[0178] Calculate document accuracy;

[0179] in, is the document accuracy, is the weight coefficient of the cross-validation score, is the weight coefficient of the consistency analysis score, is the weight coefficient of the data integrity score, is the cross-validation score of the document information, is the consistency analysis score of the document information, is the completeness score of a single data field, is the total number of data fields, is the index of the data field;

[0180] formula:

[0181] ;

[0182] Parameter meaning and acquisition method:

[0183] : is the cross-validation score, obtained by comparing the diagnosis information in the medical records with the image annotation information;

[0184] : It is the consistency analysis score, which is calculated by comparing the matching degree between the diagnosis results and treatment measures in the medical records;

[0185] : It is the completeness score of a single data field, obtained by checking whether each field in the medical record is completely filled out, including the patient's name, gender, age information, symptom description, and diagnosis results;

[0186] : is the total number of data fields, reflecting the total number of fields that need to be filled in the medical record;

[0187] , , : is the weight coefficient of the corresponding parameter;

[0188] Calculation example:

[0189] set up , , , , , , ;

[0190] calculate :

[0191] ;

[0192] ;

[0193] ;

[0194] ;

[0195] The result 52.9 reflects the comprehensive accuracy and completeness of the medical record data. The value is used to determine whether the medical record data meets the preset accuracy and completeness standards, and further decide on the processing and use of the medical record data.

[0196] Based on document accuracy, detect and correct data entry errors, including correcting inconsistent information, filling in missing information, and generating verified medical record data;

[0197] Use data correction tools, such as the update function of the database management system, combined with customized data correction scripts, including automatic correction of inconsistent and missing information. The script is executed in the database, and for each identified error or missing point, the medical record is updated to reflect more accurate information. For complex contradictions that cannot be resolved automatically, they are marked and a report is generated for manual review by medical staff. After the operation is completed, the system generates verified medical record data, including corrected and filled information, to ensure the integrity and accuracy of the medical record data.

[0198] See also Figure 7 , the specific steps for obtaining the target user's behavior pattern are:

[0199] Based on the verified medical record data, monitor the user's input behavior, including collecting the user's keyboard typing speed and mouse movement pattern data to obtain user behavior data;

[0200] The user's keyboard typing speed and mouse movement pattern in the electronic health record system are recorded and analyzed, and input data is collected in real time. The keyboard typing speed is obtained by calculating the average time interval between each keyboard tapping, and the mouse movement speed is measured by tracking the number of pixels the mouse moves per second. The collected data is encrypted and sent to the central server for analysis to obtain a data set including the behavioral characteristics of each user. The target data set includes key parameters such as timestamp, typing speed, movement speed, etc., which provides basic data for behavioral analysis.

[0201] According to user behavior data, through the formula:

[0202] ;

[0203] Calculate the characteristic value of user behavior;

[0204] in, is the variability score of the target user's behavior pattern, It is The speed of each keyboard stroke, is the average keyboard typing speed, It is The speed of mouse movement. is the average mouse movement speed, is the measured number of keyboard strokes, is the number of measurements of mouse movement, is the iteration index of keyboard strokes, is the iteration index of the mouse movement;

[0205] formula:

[0206] ;

[0207] Parameter meaning and acquisition method:

[0208] : No. The speed of each keyboard stroke is obtained by using monitoring software to record the interval between each stroke in real time;

[0209] : The average value of keyboard typing speed;

[0210] : No. The speed of each mouse movement is obtained by real-time recording of the rate of each mouse movement using monitoring software;

[0211] : The average value of mouse movement speed;

[0212] : The measured number of keyboard strokes, that is, the total number of keystrokes recorded within the monitoring time window;

[0213] : The number of mouse movement measurements, that is, the total number of movements recorded within the monitoring time window;

[0214] , : Iteration indicators, representing the number of keyboard taps and mouse movements;

[0215] Calculation example:

[0216] set up , , keyboard typing speed Milliseconds / click, mouse movement speed Pixels / second, Milliseconds / hit, Pixels / second, calculate variability:

[0217] Compute the square of the difference of each term for each keystroke:

[0218] ;

[0219] ;

[0220] ;

[0221] ;

[0222] ;

[0223] ;

[0224] ;

[0225] ;

[0226] ;

[0227] ;

[0228] For mouse movement, calculate the square of the difference of each term:

[0229] ;

[0230] ;

[0231] ;

[0232] ;

[0233] ;

[0234] ;

[0235] ;

[0236] ;

[0237] ;

[0238] ;

[0239] ;

[0240] ;

[0241] Calculate the standard deviation:

[0242] ;

[0243] ;

[0244] ;

[0245] What you get The value is 3.34. The numerical value indicates the consistency and regularity deviation of user behavior. The lower the value, the more consistent the user's behavior. The calculation process is used to evaluate and identify the behavior patterns of target users and provide support for security monitoring.

[0246] Based on the characteristic values ​​of user behavior, evaluate the consistency and regularity of user behavior, analyze the stability of user behavior, and identify the behavior patterns of target users;

[0247] Use statistical analysis methods and machine learning models to evaluate the consistency and regularity of user behavior and identify user behavior patterns. Use time series analysis and clustering algorithms, such as K-means. Time series analysis is applied to data on keyboard typing speed and mouse movement speed to identify any periodic changes or abnormal patterns in the behavior data. Input time series data into the clustering algorithm to classify user behavior and identify typical behavior patterns or abnormal behavior. The analysis results help system administrators and security teams understand the typical behavior of users and its changes, and evaluate the stability of behavior. Each user's behavior pattern is recorded in detail and compared with the user's historical behavior data to identify any significant behavior changes or inconsistencies.

[0248] See also Figure 8 , the specific steps for obtaining access control parameters are:

[0249] Based on the behavior pattern of the target user, extract the behavior pattern information associated with the user's login identity, including keyboard tapping patterns and mouse operation habits, and obtain the login identity behavior feature data;

[0250] Through the behavior monitoring software deployed on the user terminal, the tapping interval, tapping force, mouse movement speed, and acceleration parameters are recorded. Each time the user logs in, the software is automatically activated to seamlessly record the behavior data. The target data is transmitted to the central analysis server through an encrypted channel. The data processing script running on the server is written in Python, and the Pandas library is used for data cleaning and feature extraction. Mathematical operations are performed through the NumPy library to accurately calculate the statistical characteristics of the behavior pattern. The obtained behavioral feature data include the average keyboard tapping interval of each user, the average and standard deviation of the mouse movement speed, etc. The target data provides a basis for identifying the user's login identity.

[0251] According to the login identity behavior characteristic data, use the formula:

[0252] ;

[0253] Calculate the consistency between the current access request behavior and the user's standard behavior pattern to obtain a behavior consistency score;

[0254] in, Indicates the consistency between the user's current behavior and historical behavior patterns. Represents the user's standard behavior pattern The behavior characteristic value of the event, Represents the first The behavior characteristic value of the event, represents the total number of behavioral events recorded in a given monitoring period, is the index of the behavior feature event;

[0255] formula:

[0256] ;

[0257] Parameter meaning and acquisition method:

[0258] : The user's first The sub-behavior feature value is obtained through the user behavior monitoring system and reflects the typical operation speed of the user;

[0259] : The current access request The behavior feature value is also obtained through real-time monitoring and reflects the user's current operation speed;

[0260] : The number of behavioral events, obtained by analyzing user activity data within the target time window;

[0261] Calculation example:

[0262] Set the target operation user's keyboard tapping speed in multiple interfaces to , the keyboard tapping speed of the standard behavior mode is , , calculate the behavioral consistency:

[0263] calculate :

[0264] ;

[0265] calculate and The sum of squares:

[0266] ;

[0267] ;

[0268] calculate :

[0269] ;

[0270] ;

[0271] The calculation result is 0.99971, which shows that the user's current behavior is very similar to the historical standard pattern, reflecting that the user's current access request is legal. The calculation process is used to verify the user's identity and adjust the access rights.

[0272] Based on the behavior consistency score, the legitimacy of the access request is evaluated in real time, and the data access rights of the operating user are adjusted, including access restriction and identity authentication, to obtain access control parameters;

[0273] Use the anomaly detection model in machine learning, including the classification algorithm based on support vector machine, to train the model from historical behavior data and define the boundaries of normal behavior. When the user logs in, the behavior data is captured in real time and quickly scored for consistency through this model. If the user's current behavior is highly consistent with the pattern in the training set, the behavior consistency score will be higher than the preset threshold, and the user will have normal access. If the consistency score is lower than the threshold, the system will trigger security response measures, such as sending warnings, requiring secondary identity authentication, or restricting access. This evaluation process runs in the form of microservices on the server background, ensuring the system's response speed and the security of data processing. The result of the process is the generation of access control parameters. The target parameters record the evaluation results and measures taken for each access request, providing real-time data support for the system's security monitoring.

[0274] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An electronic health record management system, characterized in that: The system comprises: The image data analysis module uses edge detection based on the patient's medical image information to identify the organ and tissue types in the image. It combines the image's shooting date and the type of shooting device to match the annotation information and retrieval tags for the medical image and generate an image feature index. The medical record information classification module analyzes the text in the user's medical record based on the image feature index, identifies multiple keywords in the medical record, and automatically marks and classifies the medical record information according to the symptom characteristics and diagnosis results of multiple patients to generate a medical record classification result; The input error detection module uses the medical record classification results to analyze the data consistency of information from multiple data sources in the target medical record, combines cross-validation of image annotation information and data integrity check, detects data input errors, and corrects the input data of the medical record to generate verified medical record data; Based on the verified medical record data, the data access management module monitors the user's input behavior, identifies the target user's behavior pattern, compares it with the behavior pattern of the user's login identity, evaluates the legitimacy of the access request, adjusts the data access rights, and obtains the access control parameters.

2. The electronic health record management system according to claim 1, characterized in that: The steps for acquiring the organ and tissue types in the image are specifically as follows: Based on the patient's medical image information, the input image data is analyzed and the formula ； Calculate the gradient amplitude of the image to obtain the pixel edge intensity value; in, is the gradient amplitude of the image, Represents the gray value The partial derivative of is the gray value of the image, is the horizontal coordinate of the image, is the vertical coordinate of the image; Based on the pixel edge intensity value, the contrast of the image is adjusted to mark the edge information in the image, thereby obtaining an edge information extraction record; Based on the edge information extraction record, multiple organs and tissues in the image, including the heart and lungs, are identified by analyzing the combined features of the edge information, and organ and tissue types are generated.

3. The electronic health record management system according to claim 2, characterized in that: The steps of obtaining the image feature index are specifically as follows: matching annotation information for a plurality of identified tissues and organs, including a heart and a lung, according to the organ and tissue types, and generating an annotation information matching record; Based on the matching records of the annotation information, combined with the shooting date and shooting device type of the medical image, the formula ； Calculate the index values ​​of multiple images and obtain the image index list; in, is the image feature index value, represents the first elements, is corresponding to The weight coefficient of is the eigenvector The number of elements in , is the index of the element in the feature vector; According to the image index list, the medical images are stored in a database, and the image index values ​​and storage paths are associated to generate image feature indexes.

4. The electronic health record management system according to claim 1, characterized in that: The steps for obtaining the multiple keywords in the medical record are specifically as follows: Based on the image feature index, preprocess the medical record text, including word segmentation, removing stop words, calculating the frequency of occurrence of each word in multiple documents, and counting the number of documents containing the target word to obtain a text word data set; Using the text word dataset, use the formula ； Calculate the text importance index of multiple words in a document; in, Expressive words In the documentation The text importance index in , Words In the documentation The number of occurrences in Represents any word In the documentation The number of occurrences in is the index of the target vocabulary, is any word in the document, is the index of the target document, is the total number of documents in the document set, Contains words The number of documents; According to the text importance index, multiple keywords in the medical record are identified by comparing the importance indexes of multiple words.

5. The electronic health record management system according to claim 4, characterized in that: The steps for obtaining the medical record classification results are specifically as follows: Based on the multiple keywords in the medical records, by analyzing the medical record documents and expert evaluation, evaluating the importance of multiple keywords in multiple categories, matching weights for multiple medical record categories and keywords, and establishing a keyword weight list; Based on the keyword weight list, by formula ； Calculate the scores of the target document in multiple categories to obtain document category information; in, For Documentation The keyword set in For keywords For classification The weight of For keywords In the documentation The text importance index in , For Documentation Categorized into categories, Used to determine the value of a variable that maximizes a given function. An index for medical record classification. For the target document, For Documentation Keywords in; Based on the document category information, the medical records of multiple patients are classified and archived to generate medical record classification results.

6. The electronic health record management system according to claim 1, characterized in that: The steps for obtaining the verified medical record data are specifically as follows: Based on the medical record classification results, analyzing information from multiple data sources in the target medical record, including analyzing the consistency of the diagnosis results and treatment information, identifying inconsistencies and contradictions, and generating document error identification results; Based on the document error recognition results, cross-validating the image data with the text data of the associated medical records, including comparing the consistency of the pathological features in the text with the type information of the organs and tissues recorded in the image annotations, and generating a cross-validation result; Based on the cross-validation results, combined with data integrity check, the formula ； Calculate document accuracy; in, is the document accuracy, is the weight coefficient of the cross-validation score, is the weight coefficient of the consistency analysis score, is the weight coefficient of the data integrity score, is the cross-validation score of the document information, is the consistency analysis score of the document information, is the completeness score of a single data field, is the total number of data fields, is the index of the data field; Based on the document accuracy, data input errors are detected and corrected, including correcting inconsistent information and filling in missing information, to generate verified medical record data.

7. The electronic health record management system according to claim 1, characterized in that: The steps for acquiring the behavior pattern of the target user are specifically as follows: Based on the verified medical record data, monitoring the user's input behavior, including collecting the user's keyboard typing speed and mouse movement pattern data to obtain user behavior data; According to the user behavior data, through the formula ； Calculate the characteristic value of user behavior; in, is the variability score of the target user's behavior pattern, It is The speed of each keyboard stroke, is the average keyboard typing speed, It is The speed of mouse movement. is the average mouse movement speed, is the measured number of keyboard strokes, is the number of measurements of mouse movement, is the iteration index of keyboard strokes, is the iteration index of the mouse movement; Based on the characteristic values ​​of the user behaviors, the consistency and regularity of the user behaviors are evaluated, the stability of the user behaviors is analyzed, and the behavior patterns of the target users are identified.

8. The electronic health record management system according to claim 7, characterized in that: The steps for obtaining the access control parameters are specifically as follows: Based on the behavior pattern of the target user, extract the behavior pattern information associated with the user login identity, including keyboard tapping pattern and mouse operation habits, and obtain the login identity behavior feature data; According to the login identity behavior characteristic data, use the formula ； Calculate the consistency between the current access request behavior and the user's standard behavior pattern to obtain a behavior consistency score; in, Indicates the consistency of the user's current behavior with the historical behavior pattern. Represents the user's standard behavior pattern The behavioral characteristic value of the event, Represents the first The behavioral characteristic value of the event, represents the total number of behavioral events recorded in a given monitoring period, is the index of the behavior feature event; According to the behavior consistency score, the legitimacy of the access request is evaluated in real time, and the data access rights of the operating user are adjusted, including restricting access and identity authentication, to obtain access control parameters.

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