Early myocardial injury monitoring system based on medical internet of things

Abstract

The invention relates to the technical field of data analysis, and discloses an early myocardial injury monitoring system based on medical internet of things. The system comprises: a quality control module, which treats a peripheral blood sample based on immunochromatography and analyzes a detection result; the extraction module completes data structuring and integrates the data with biomarker data; the encryption module realizes secure data transmission; the classification module performs labeling classification and standardization processing; the evaluation module executes risk evaluation and space-time analysis; and the grading module grades the early warning data and feeds back and stores the data. Early accurate monitoring of myocardial injury is achieved, disease change is found in time, risk early warning is carried out, and therefore the out-of-hospital medical treatment delay time is shortened.

Description

Technical Field

[0001] The present application relates to the field of data analysis, and in particular to an early myocardial injury monitoring system based on the medical Internet of Things. Background Art

[0002] Cardiovascular disease is one of the major diseases that seriously threaten human health, and it is showing a trend of becoming younger. Myocardial damage is an important manifestation of cardiovascular disease, and its causes are complex, including insufficient blood or oxygen supply to myocardial cells, drug toxicity, strenuous exercise, etc. When myocardial damage occurs, the left ventricle releases specific proteins into the blood, such as creatine kinase and its subtypes, cardiac troponin, etc. At present, medical institutions mainly determine the degree of myocardial damage by testing these biomarkers.

[0003] Existing methods for monitoring myocardial injury have obvious shortcomings: the detection process relies on medical institutions to complete, which results in time delays; patients often seek medical treatment only after experiencing obvious symptoms, thus missing the best time for treatment; commonly used rapid detection methods such as the myocardial injury quadruple test card lack sensitivity and are prone to false positives; there is a lack of comprehensive analysis and real-time monitoring capabilities for multiple biomarkers, making it impossible to provide timely warnings and interventions. Summary of the invention

[0004] The present application provides an early myocardial injury monitoring system based on the medical Internet of Things, which is used to achieve early and accurate monitoring of myocardial injury, timely detect changes in the condition and provide risk warnings, thereby reducing the delay time for out-of-hospital medical treatment.

[0005] In a first aspect, the present application provides an early myocardial injury monitoring system based on the medical Internet of Things, and the early myocardial injury monitoring system based on the medical Internet of Things includes: The quality control module is used to perform antigen-antibody reaction processing on peripheral blood samples by immunochromatography, and perform optical density analysis according to the color development degree of the test line and the quality control line to obtain quantitative data of myocardial injury biomarkers; An extraction module is used to perform structured extraction of the patient's basic characteristic information and clinical history information according to a preset information collection template, and to associate and integrate the information with the quantitative data of myocardial injury biomarkers to obtain an initial myocardial injury monitoring data set; An encryption module is used to encrypt the initial myocardial injury monitoring data set according to an asymmetric encryption algorithm, generate an encrypted data packet through a data compression algorithm, transmit and decrypt and decompress it through the Internet of Things protocol, and obtain the original myocardial injury monitoring data; The classification module is used to label and classify the original myocardial injury monitoring data through a hierarchical clustering algorithm, and to perform data calibration and standardization processing in combination with reference standard values ​​in a multi-source database to form a myocardial injury analysis data set; The evaluation module is used to perform multi-parameter risk assessment on the myocardial injury analysis data set based on the deep learning model, perform spatiotemporal correlation analysis in combination with geographic location information, and generate real-time myocardial injury risk warning data; The grading module is used to grade the real-time myocardial injury risk warning data according to the warning level assessment rules, feed back to the terminal through data encryption transmission, and store the assessment results in the database to form dynamic monitoring records.

[0006] In the technical solution provided by the present application, the quality control module uses immunochromatography to process antigen-antibody reactions, and performs optical density analysis through the color development of the test line and the quality control line, which can accurately obtain the quantitative data of myocardial injury biomarkers, ensuring the reliability and accuracy of the test results. The extraction module uses a preset information collection template to perform structured extraction of the patient's basic characteristic information and clinical history information, and associates and integrates it with the quantitative data of myocardial injury biomarkers, effectively solving the problem of data fragmentation and improving the integrity and practicality of the data. The encryption module uses an asymmetric encryption algorithm to encrypt the data, and generates an encrypted data packet through a data compression algorithm, ensuring the security and efficiency of the data transmission process. The classification module uses a hierarchical clustering algorithm to label and classify the original data, and combines the reference standard values ​​in the multi-source database for data calibration and standardization, which improves the accuracy and scientificity of data analysis. The evaluation module performs multi-parameter risk assessment based on a deep learning model, and combines geographic location information for spatiotemporal correlation analysis, realizing accurate prediction and timely warning of myocardial injury risks. The grading module performs grading processing according to the warning level assessment rules, feeds back the assessment results to the terminal through encrypted transmission, and stores the results in the database to form dynamic monitoring records, providing a reliable basis for medical decision-making. This modular design not only realizes the full process automation management from data collection, processing, analysis to warning, but also ensures patient privacy and data security through multiple data protection mechanisms. At the same time, the application of deep learning algorithms improves the accuracy of myocardial injury risk assessment. The real-time monitoring and early warning functions of the system can help medical staff to timely detect potential myocardial injury risks and take corresponding preventive and intervention measures, which is of great significance to improving the diagnosis and treatment of cardiovascular diseases. In addition, the system's Internet of Things architecture design enables data to be transmitted and shared remotely, facilitates collaboration between medical institutions, and improves the utilization efficiency of medical resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0008] Figure 1 This is a schematic diagram of an embodiment of an early myocardial injury monitoring system based on the medical Internet of Things in the embodiments of the present application.

[0009] Figure 2 Schematic diagram of the structure of the grading module 106 in the early myocardial injury monitoring system based on the medical Internet of Things in the embodiment of the present application. DETAILED DESCRIPTION

[0010] The embodiment of the present application provides an early myocardial injury monitoring system based on the medical Internet of Things. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0011] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of an early myocardial injury monitoring system based on the medical Internet of Things includes: The quality control module 101 is used to perform antigen-antibody reaction processing on the peripheral blood sample by immunochromatography, and perform optical density analysis according to the color development degree of the detection line and the quality control line to obtain quantitative data of myocardial injury biomarkers; The extraction module 102 is used to perform structured extraction of the patient's basic characteristic information and clinical history information according to a preset information collection template, and associate and integrate them with the quantitative data of myocardial injury biomarkers to obtain an initial myocardial injury monitoring data set; The encryption module 103 is used to encrypt the initial myocardial injury monitoring data set according to the asymmetric encryption algorithm, generate an encrypted data packet through the data compression algorithm, transmit and decrypt and decompress it through the Internet of Things protocol, and obtain the original myocardial injury monitoring data; The classification module 104 is used to classify the original myocardial injury monitoring data by labeling through a hierarchical clustering algorithm, and to perform data calibration and standardization processing in combination with reference standard values ​​in a multi-source database to form a myocardial injury analysis data set; An evaluation module 105 is used to perform a multi-parameter risk evaluation on the myocardial injury analysis data set according to a deep learning model, perform a spatiotemporal correlation analysis in combination with geographic location information, and generate real-time myocardial injury risk warning data; The grading module 106 is used to grade the real-time myocardial injury risk warning data according to the warning level assessment rules, feed back to the terminal through data encryption transmission, and store the assessment results in the database to form a dynamic monitoring record.

[0012] It is understandable that the execution subject of the present application can be an early myocardial injury monitoring system based on the medical Internet of Things, or a terminal or a server, which is not limited here. The present application embodiment is described by taking the server as the execution subject as an example.

[0013] Specifically, the early myocardial injury monitoring system based on the medical Internet of Things realizes early monitoring of myocardial injury through the collaborative work of multiple functional modules. The quality control module 101 is responsible for the collection and detection of biological samples. A glass fiber membrane is selected to make a sample pad, and it is assembled to a sticky backing plate by an adhesive. The gold-labeled mouse anti-human cMyBP-C antibody dilution is sprayed on the colloidal gold-labeled conjugate pad by a film scribing instrument, and a detection line coated with cMyBP-C monoclonal antibody is drawn on the nitrocellulose membrane. The quality control line is located 5 mm above the detection line and is composed of rabbit anti-mouse IgG antibodies. The collected peripheral blood sample is dripped into the sample addition hole of the test card, and chromatographic migration is carried out by capillary action. After the antigen and antibody undergo specific binding, color is developed at the detection line and the quality control line. The color intensity is quantitatively analyzed by an optical density analyzer to obtain the concentration data of the myocardial injury biomarker. The extraction module 102 collects and structures information, and performs field mapping and normalization on the patient's basic information (including patient number, gender, age, height, weight, and location) and clinical history information (including previous history of cardiovascular disease, history of other diseases, history of surgical trauma, and history of allergies). The processed structured information is matched and associated with the quantitative data of biomarkers according to the timestamp, and a unique identifier is added to form an initial myocardial injury monitoring data set.

[0014] The encryption module 103 uses the RSA asymmetric encryption algorithm to process the data securely. Generate a public key and a private key pair, and use the public key to encrypt the data in blocks. The size of each data block is 256 bytes, and a hash value is calculated for each data block as a digital signature. The encrypted data is compressed by Huffman coding, and the compressed data packet is transmitted through the Internet of Things protocol (WiFi, NFC or GPS). The receiving end uses the private key to decrypt the data packet and restore the original myocardial injury monitoring data. The classification module 104 uses a hierarchical clustering algorithm to classify the original monitoring data. A distance matrix is ​​constructed based on the characteristics of biomarker concentration values, change trends, time series, etc., and Ward's minimum variance method is used for clustering. The clustering results are compared and calibrated with the standard data in the myocardial injury marker reference value database, cardiovascular disease database, and medical record database, and numerical standardization is performed to form a myocardial injury analysis data set with a unified dimension.

[0015] The evaluation module 105 constructs a risk assessment model based on a deep learning framework. The input layer receives a myocardial injury analysis data set, extracts time series features through a multi-layer convolutional neural network, performs spatiotemporal correlation analysis in combination with geographic location information, and calculates the weight coefficients of various risk factors. The evaluation indicators include the degree of abnormal concentration of biomarkers, the speed of symptom aggravation, geographical environmental factors, etc., and generate real-time myocardial injury risk warning data after comprehensive scoring. The grading module 106 sets the risk level threshold according to the ischemic time standard of the European Society of Cardiology Guidelines. The weights of various indicators in the real-time warning data are calculated, and a comprehensive score is performed in combination with the patient's medical history. The risk level is divided into mild (within 1 hour), moderate (1-2 hours) and severe (more than 2 hours) according to the total ischemic time of the heart. A corresponding warning strategy is set for each level interval to generate feedback information containing risk prompts. At the same time, the evaluation results are fed back to the medical terminal and the patient terminal through encrypted transmission, and the complete records are stored in the medical record database.

[0016] For example, when peripheral blood samples are tested by immunochromatographic test cards, the normal color of the quality control line indicates that the test is effective, and the color intensity of the test line is quantitatively analyzed to show that the concentration of troponin is 2.5 ng / mL. Combined with the patient's basic information and previous history of coronary heart disease, the system generates an initial monitoring data set. The data is encrypted and compressed and transmitted through the Internet of Things, and the receiving end decrypts the original data. The classification module clusters the data into a high-risk group, and the evaluation module calculates a comprehensive risk score of 0.85. Based on the time from the patient's current location to the nearest hospital and the duration of the symptoms, the system determines that it is a moderate risk, and immediately sends warning information to medical institutions and patients, suggesting timely medical treatment.

[0017] In the embodiment of the present application, the quality control module uses immunochromatography to process antigen-antibody reactions, and performs optical density analysis through the color development of the test line and the quality control line, so as to accurately obtain the quantitative data of myocardial injury biomarkers, thereby ensuring the reliability and accuracy of the test results. The extraction module uses a preset information collection template to perform structured extraction of the patient's basic characteristic information and clinical history information, and associates and integrates it with the quantitative data of myocardial injury biomarkers, effectively solving the problem of data fragmentation and improving the integrity and practicality of the data. The encryption module uses an asymmetric encryption algorithm to encrypt the data, and generates an encrypted data packet through a data compression algorithm, thereby ensuring the security and efficiency of the data transmission process. The classification module uses a hierarchical clustering algorithm to label and classify the original data, and combines the reference standard values ​​in the multi-source database for data calibration and standardization, thereby improving the accuracy and scientificity of data analysis. The evaluation module performs multi-parameter risk assessment based on a deep learning model, and combines the spatial-temporal correlation analysis with the geographic location information, thereby realizing accurate prediction and timely warning of myocardial injury risks. The grading module performs grading processing according to the warning level assessment rules, feeds back the assessment results to the terminal through encrypted transmission, and stores the results in the database to form dynamic monitoring records, providing a reliable basis for medical decision-making. This modular design not only realizes the full process automation management from data collection, processing, analysis to warning, but also ensures patient privacy and data security through multiple data protection mechanisms. At the same time, the application of deep learning algorithms improves the accuracy of myocardial injury risk assessment. The real-time monitoring and early warning functions of the system can help medical staff to timely detect potential myocardial injury risks and take corresponding preventive and intervention measures, which is of great significance to improving the diagnosis and treatment of cardiovascular diseases. In addition, the system's Internet of Things architecture design enables data to be transmitted and shared remotely, facilitates collaboration between medical institutions, and improves the utilization efficiency of medical resources.

[0018] In a specific embodiment, the quality control module 101 is used to: (1) The peripheral blood sample is chromatographically migrated through the capillary effect to form an immune response flow; (2) Capture and enrich the immune reaction flow based on the specific binding of antigen and antibody to generate color reaction strips; (3) Determine the optical density of the color reaction band based on the colorimetric method to obtain the gray value of the band; (4) Perform numerical standardization on the grayscale value of the strip to obtain the standard optical density value; (5) Calculate the standard optical density value through the standard curve to convert the concentration to form concentration data; (6) Quantitatively analyze myocardial injury biomarkers based on the concentration data to obtain quantitative data of myocardial injury biomarkers.

[0019] Specifically, the detection process of myocardial injury biomarkers by the quality control module 101 starts with sampling. When the peripheral blood sample is dripped onto the sample pad, due to the special capillary structure of the glass fiber membrane, the blood sample produces horizontal chromatographic migration along the nitrocellulose membrane under the action of the capillary effect. In the immune reaction flow formed by the chromatographic migration process, myocardial specific proteins in the blood (including creatine kinase and its MB subtypes, troponin I, T, and myosin binding protein) begin to specifically bind to the gold-labeled antibody. As the immune reaction flow continues to migrate, the gold-labeled antibody-antigen complex is captured and enriched by the fixed monoclonal antibody at the detection line. At the same time, the excess gold-labeled antibody will continue to migrate forward to the quality control line and bind to the fixed rabbit anti-mouse IgG antibody. This dual-line design ensures the verification of the effectiveness of the detection, and only when the quality control line is colored does it indicate that the detection process is effective. The specific immune reaction at the detection line and the quality control line causes the gold-labeled particles to aggregate there, forming a red color reaction band visible to the naked eye.

[0020] The principle of colorimetry is used for the quantitative analysis of the color reaction strips, and the strips are scanned by an optical density instrument. The optical density meter emits a light beam of a specific wavelength to illuminate the color strips, measures the intensity of the transmitted light, converts the optical signal into an electrical signal, and then obtains a digital signal through analog-to-digital conversion to obtain the grayscale value data of the strips. The grayscale value usually ranges from 0 to 255. The larger the value, the darker the color, indicating that the concentration of the bound marker is higher. When standardizing the grayscale values ​​of the obtained strips, background interference needs to be eliminated. By measuring the grayscale value of the nitrocellulose membrane background as the background value, the grayscale value of the detection line is subtracted from the background value to obtain the actual optical density value. In order to facilitate the comparison of results from different batches, the optical density value is normalized to obtain the standard optical density value. The calculation formula for the standard optical density value is: ; in, is the standard optical density value, is the gray value of the detection line, is the background gray value, is the full-scale grayscale value, is the correction factor.

[0021] When converting the standard optical density value to the actual biomarker concentration, a standard curve needs to be established. By measuring a series of standards with known concentrations, the corresponding relationship between the standard optical density value and the concentration is obtained. The calculation formula for concentration conversion is: ; in, is the biomarker concentration value, is the standard optical density value, is the exponential growth coefficient, is the exponential decay coefficient, is the linear growth coefficient, is the linear attenuation coefficient, is the baseline correction coefficient. These parameters are obtained by fitting the results of the standard. Based on the converted concentration data, the myocardial injury biomarkers are quantitatively analyzed in combination with the clinical diagnostic criteria. The quantitative data of myocardial injury biomarkers are formed by analyzing the changing trend, peak value, duration and other characteristic parameters of the marker concentration. For example: when a peripheral blood sample is dripped onto the test card, the sample migrates under the action of capillaries and combines with the gold-labeled antibody to form an immune complex. At the detection line, the biomarker labeled with the gold-labeled antibody is captured to form a red strip. The gray value of the detection line is 180, the background gray value is 20, and the full-scale gray value is 250 measured by the optical density meter. After standardization, the standard optical density value of 0.696 is obtained. The optical density value is substituted into the standard curve equation to calculate the actual concentration of the biomarker. Combined with the patient's clinical manifestations and other test indicators, a quantitative assessment of the degree of myocardial injury is completed.

[0022] In a specific embodiment, the extraction module 102 is used to: (1) Convert the basic feature information into key-value pairs through field mapping to obtain structured basic feature data; (2) Convert clinical history information into time series records through time series coding to obtain structured clinical history data; (3) Timestamp merging of structured basic feature data and structured clinical history data to generate time series feature data; (4) Through data verification, missing values ​​in the time series feature data are supplemented to obtain complete feature data; (5) Align the complete feature data and the quantitative data of myocardial injury biomarkers to form associated feature data; (6) The associated feature data are merged through data identification to obtain the initial myocardial injury monitoring data set.

[0023] Specifically, the extraction module 102 performs field mapping to establish a key-value pair relationship of basic feature information. In the specific process, basic information such as patient number, gender, age, height, weight, location, etc. is converted according to a standardized format. Field mapping is stored in JSON format, and each field contains attribute information such as field name, field type, and value range. For example, the gender field is mapped to {field:"gender", type:"string", value:["male","female"]}, and the age field is mapped to {field:"age", type:"integer", range:[0,120]}. Through this field mapping, the unstructured basic feature information is converted into a standardized key-value pair data structure.

[0024] For the time series record conversion of clinical medical history information, time series coding is used to process medical information such as previous history of cardiovascular disease, other disease history, surgical trauma history, allergy history, etc. Each medical history record contains information such as the time of occurrence, disease type, severity, and treatment plan. Time series coding converts this information into structured data with timestamps in the form of [timestamp, event_type, severity, treatment]. This coding method facilitates subsequent time series analysis and association mining of medical history data. When merging the structured basic feature data and clinical medical history data with timestamps, a unified time reference needs to be established. Taking the time of the patient's first visit as the reference point, all time information is converted into relative time. Data from different sources are aligned in the time dimension through timestamps to form feature data containing complete time series information. Each time point in the time series feature data is associated with the basic feature state and clinical medical history events at that moment.

[0025] When performing data verification on time series feature data, the focus is on dealing with missing values. For missing numerical features, the average value of adjacent time points is used to fill in; for missing categorical features, the nearest neighbor interpolation method is used to supplement. Data verification also includes outlier detection, marking and correcting data that exceeds the normal range. These processes ensure the integrity and validity of feature data. The complete feature data needs to be aligned with the quantitative data of myocardial injury biomarkers. The quantitative data of biomarkers contains the detection time and the corresponding concentration values, and these data are matched with the feature data according to the time axis. For cases where the detection time point does not completely overlap with the feature data time point, the time window matching method is used to associate the data at the closest time point.

[0026] The associated feature data are merged through data identification. Each data record is assigned a unique identifier, which contains information such as patient ID, recording time, data type, etc. All related fields are organized in a predefined format to form an initial myocardial injury monitoring data set with a unified structure. This data set contains the complete information spectrum of the patient.

[0027] For example, when a patient is monitored for myocardial injury, basic feature information is collected, such as patient number P20240122001, male, 45 years old, etc., and converted into a standardized key-value pair format through field mapping. At the same time, medical history information such as the patient's diagnosis of coronary heart disease five years ago and stent implantation surgery three years ago is collected, and this information is converted into structured clinical history data through time series encoding. The system establishes a timeline based on the time of admission, and aligns the basic feature data and medical history data in time. During the processing, it was found that the blood pressure data was missing, and it was supplemented by data from nearby time points. The myocardial injury biomarker test performed on the same day showed an elevated troponin concentration, and this test result was associated with other feature data. All data are organized by unique identifiers to form a monitoring data set.

[0028] In a specific embodiment, the encryption module 103 is used to: (1) Obtain quantitative data of myocardial injury biomarkers and patient clinical information data in the initial myocardial injury monitoring data set; (2) inputting the quantitative data of myocardial injury biomarkers and the clinical information data of the patient into the data encryption management model to generate a segmented encryption scheme matching the myocardial injury monitoring, wherein the segmented encryption scheme includes a biomarker data encryption segment, a clinical information data encryption segment, and an overall encryption path consisting of a plurality of encryption segments, and setting corresponding data verification parameters for each data encryption segment; (3) Perform RSA encryption on each data encryption segment and verify the encryption validity based on the data verification parameters; (4) performing block compression on the encrypted data to generate an encrypted data packet with a check bit, wherein the encrypted data packet includes encryption position information of the data encryption segment, encryption parameter information, and data check information; (5) Transmit the encrypted data packet in blocks through the network protocol and perform data integrity verification at the receiving end; (6) Decrypt the encrypted data packet according to the encrypted position information and the encrypted parameter information to obtain the original myocardial injury monitoring data.

[0029] Specifically, the encryption module 103 separates two types of key data in the initial myocardial injury monitoring data set for secure data transmission processing: quantitative data of myocardial injury biomarkers (including troponin concentration value, detection time, detection sequence number) and patient clinical information data (including basic feature information, medical history records, and treatment records). The separated data is classified and marked according to the data type and sensitivity, which is convenient for subsequent differentiated encryption processing. The classified and marked data is input into the data encryption management model, which automatically generates a segmented encryption scheme based on the data characteristics. The biomarker data encryption segment specifically processes quantitative detection data, and packages the detection value, timestamp, and sequence number into a data block of fixed length; the clinical information data encryption segment processes patient information, and encodes and packages text data and numerical data respectively. The total encryption path specifies the order and hierarchy of data encryption, and prioritizes the encryption of biomarker data with high sensitivity, followed by the processing of clinical information data. Verification parameters are set for each data encryption segment, including a data length check code, a cyclic redundancy check code (CRC), and a hash check value.

[0030] RSA encryption uses a 1024-bit key pair to encrypt each data encryption segment in blocks. Generate a random session key and encrypt the session key with the recipient's public key. Then use the session key to symmetric encrypt the data, and each encryption block is 117 bytes in size (to avoid the RSA encryption limit of more than 128 bytes after padding). Digital signatures are calculated for the encrypted data block and session key ciphertext respectively, and together with the verification parameters, form an integrity verification chain. The encrypted data is compressed in blocks and compressed using the DEFLATE algorithm. The compression process represents adjacent repeated data with shorter codes while retaining the necessary decompression dictionary. The compressed data contains three main parts: header information (recording encryption position and parameter information), data body (compressed encrypted data) and verification information (including CRC code and digital signature). The size of each compressed block is controlled within 1KB to ensure transmission efficiency.

[0031] The data packet is transmitted through the IoT communication protocol, supporting multiple transmission methods such as WiFi, NFC or GPS. The transmission adopts a block strategy, and each data block has a sequence number and a checksum. The receiving end reassembles the data block according to the sequence number and verifies the data integrity through the checksum. For data blocks lost or damaged during transmission, the automatic retransmission request (ARQ) mechanism is used for retransmission. The receiving end verifies the integrity of the data packet, including checking the CRC code and verifying the digital signature. After verification, decryption is performed according to the encrypted position and parameters in the header information. The session key is first decrypted with the receiver's private key, and then the decrypted session key is used to decrypt the data body. The decrypted data is decompressed and restored to the original myocardial injury monitoring data.

[0032] For example, an initial monitoring data set contains a patient's troponin quantitative test data (test value, test time, serial number) and clinical information (past medical history, medication records). The encryption management model divides the test data into primary encryption segments and clinical information into secondary encryption segments. After the encryption scheme is generated, the test data is encrypted using the RSA algorithm, and the digital signature and CRC checksum are calculated at the same time. The encrypted data is compressed to form a data packet containing header information, compressed data, and checksum information. The data packet is transmitted to the receiving end in blocks via the WiFi protocol. The receiving end verifies the integrity of the data and then decrypts and decompresses it to restore the original monitoring data.

[0033] In a specific embodiment, the classification module 104 is used to: (1) Obtaining biomarker quantitative data and medical history characteristic data from the original myocardial injury monitoring data; (2) Clustering and stratifying the biomarker quantitative data according to the concentration value range to generate multi-level concentration distribution data, where the multi-level concentration distribution data includes the baseline concentration value, change trend value, peak point and corresponding time series information of the biomarker; (3) Compare and analyze the multi-level concentration distribution data with the reference values ​​of myocardial injury markers, cardiovascular disease data, and medical record data in a multi-source database to establish a grading standard for the severity of myocardial injury; (4) Labeling the concentration distribution data based on the myocardial injury severity grading standard to generate graded data containing risk level labels; (5) Perform data calibration on abnormal numerical points in the classified data to eliminate data deviations caused by interference factors; (6) Correlation analysis is performed on the calibrated classification data and the medical history characteristic data to form a myocardial injury analysis data set.

[0034] Specifically, the classification module 104 extracts two types of core information from the original myocardial injury monitoring data: biomarker quantitative data and medical history characteristic data. The biomarker quantitative data includes the concentration values ​​of multiple markers such as creatine kinase and its MB subtype, cardiac troponin I, T, myosin binding protein, etc., and each set of data is accompanied by a sampling timestamp. The medical history characteristic data includes information such as previous cardiovascular disease history, surgical history, and medication records. When clustering and stratifying the biomarker quantitative data, a hierarchical clustering algorithm is used to process the concentration values. First, the concentration values ​​of each marker are arranged in a time series, and the concentration difference between adjacent time points is calculated to obtain a change trend sequence. The peak time is determined by finding the maximum point of the concentration value, and the concentration level corresponding to each peak is recorded at the same time. Based on these characteristic values, multi-level concentration distribution data is constructed, including the baseline concentration (initial detection value) of each marker, the change trend (rise / fall rate), the peak point (highest concentration and occurrence time), and the time series data of the entire monitoring process.

[0035] When comparing and analyzing the multi-level concentration distribution data with the multi-source database, the myocardial injury marker reference value database is first queried to obtain the standard concentration range of each marker under different severity levels. Then the cardiovascular disease database is retrieved to obtain the characteristic patterns of marker changes of different types of cardiovascular diseases. At the same time, the marker change rules of similar cases are extracted from the medical record database. By comparing and analyzing the data of these three dimensions, a grading standard for the severity of myocardial injury is established. The labeling process is to label the concentration distribution data with risk levels. Based on the established grading standard, the marker data is mapped to the corresponding risk level. In the specific process, multiple risk indicators are set: the degree of single concentration exceeding the standard (the ratio of the current concentration to the reference value), the concentration rising rate (the increase rate per unit time), and the duration (the duration of the exceeding state). Each indicator corresponds to a specific threshold interval. For example, the concentration of troponin I exceeds the normal value by 5 times and lasts for more than 2 hours, which is defined as high risk, and the rising rate exceeds 100% per hour, which is defined as acute exacerbation. By comprehensively evaluating these indicators, the data at each time point are labeled with a risk level label: mild, moderate or severe.

[0036] When calibrating the outliers in the graded data, first identify the data points that do not conform to the laws of physiological changes. Set a tolerance range and mark the data points outside the range as suspected abnormalities. Perform a traceability analysis on these abnormal points to find out whether there are interfering factors (such as detection instrument failure, sample contamination, medication interference, etc.). Abnormal data confirmed to be caused by interference will be eliminated and replaced with the average value of the adjacent valid data points. Finally, the calibrated graded data will be correlated with the medical history feature data. Based on the timeline, the graded data is matched with the patient's previous medical history, treatment records, complications and other information to analyze the correlation between marker changes and clinical events. Through this correlation analysis, a myocardial injury analysis data set containing complete monitoring data and clinical background is formed.

[0037] For example, the monitoring data of a patient includes 72 consecutive hours of troponin I concentration test values. After arranging these data in chronological order, the concentration change trend was calculated, and it was found that the first peak appeared in the 24th hour. The reference value database was compared and found that the peak value exceeded the normal range by 8 times, and the concentration increase rate reached 150% per hour in the first 4 hours, which was consistent with the characteristics of acute myocardial injury. The system automatically labeled the data for this period with a "severe risk" label. At the same time, it was found that an abnormal concentration drop occurred in the 48th hour. After verification, it was found that some drugs that interfered with the detection were used during this period, so the abnormal value was removed and replaced with the average value of the previous and next time points. These processed data are associated with the patient's coronary heart disease history, stent surgery records and other information to form an analysis data set.

[0038] In a specific embodiment, the evaluation module 105 is used to: (1) Calculate the difference between the biomarker concentration values ​​in the myocardial injury analysis data set and the reference standard range to obtain concentration deviation data; (2) Correlate the concentration deviation data with the medical history characteristic data in time series and generate a risk trend curve according to the time nodes; (3) Calculate the slope and analyze the inflection point of the risk trend curve to identify the time point when myocardial damage worsens; (4) Map the time point of myocardial injury aggravation with geographic location information to establish a correlation map between the patient's activity trajectory and the degree of myocardial injury; (5) Conduct risk factor attribution analysis on the association map and calculate the contribution weight of each risk factor; (6) Perform weighted calculation on the contribution weight and current monitoring data to generate real-time myocardial injury risk warning data.

[0039] Specifically, the evaluation module 105 first performs a difference calculation on the myocardial injury analysis data set. The concentration values ​​of the main myocardial injury biomarkers (including troponin, myoglobin, creatine kinase, etc.) are compared with the standard reference range. Each marker has a corresponding upper limit of normal value, and the deviation between the actual detection value and the upper limit is calculated. The deviation value not only includes the absolute difference, but also considers the multiples beyond the normal range, thereby forming standardized concentration deviation degree data. When the concentration deviation degree data is temporally associated with the patient's medical history characteristics, a unified time axis needs to be established. Medical history feature data includes key node information such as the time of onset of previous cardiovascular diseases and the time point of treatment intervention. By arranging the deviation data in chronological order and aligning it with the medical history events on the time axis, a risk trend curve is drawn. This curve reflects the law of change of the degree of myocardial injury over time.

[0040] The following mathematical model is used to analyze the risk trend curve: ; in, is the risk change rate at time t, is the measured value of the i-th marker at time t, is the corresponding threshold, is the weight coefficient of each marker, is the weight of the second-order derivative term, and n is the number of marker types. By calculating the maximum point and inflection point of R(t), the key time point of aggravation of myocardial injury is identified.

[0041] The identified aggravation time points are matched with GPS positioning data in time and space, and the geographical location information corresponding to each key time point is recorded. By connecting these location points, the patient's activity trajectory is formed. At the same time, the myocardial injury degree data is superimposed on the trajectory to construct a three-dimensional association map containing time, space and severity of the disease. When performing attribution analysis on the association map, risk factors in multiple dimensions are considered: environmental factors (such as air quality, altitude), activity factors (such as strenuous exercise, emotional state), basic disease factors (such as hypertension, diabetes), etc. The contribution of each factor to the aggravation of myocardial injury is determined by multivariate regression analysis, and the corresponding weight coefficient is calculated. The weight coefficients of each factor are weighted and calculated with the real-time monitoring data to obtain the current risk warning value. The level of the warning value is directly related to the determination of the warning level, thereby forming real-time myocardial injury risk warning data.

[0042] For example, the troponin I test value of a patient exceeded the upper limit of the normal value by 3 times continuously, and showed a continuous upward trend. Combined with the patient's previous history of coronary heart disease, the test data points were plotted into a risk trend curve. By calculating the slope of the curve, it was found that the slope suddenly increased at a certain time point, and an obvious inflection point appeared, indicating that the degree of myocardial damage worsened. Querying the GPS location at that time point showed that the patient was doing moderate-intensity exercise. This information was integrated into the association map, taking into account factors such as the patient's underlying disease (hypertension) and local environmental conditions (high temperature). After weight calculation, exercise intensity and underlying diseases were identified as the main risk factors. Based on the current test value and the weighted analysis of these factors, a high-risk level of early warning information was generated.

[0043] In a specific embodiment, the classification module 106 is used to: (1) Analyze the troponin and creatine kinase indicators in the real-time myocardial injury risk warning data, compare them with the standard thresholds in the myocardial injury marker reference value database, and obtain the risk score of each indicator; (2) Based on the risk score and the patient's previous cardiovascular disease history and medical history time series, a multidimensional correlation calculation is performed to generate a comprehensive risk score that includes time series characteristics; (3) The comprehensive risk score is divided into intervals according to the total cardiac ischemia time threshold, and a risk level interval map including mild, moderate and severe ischemia is established; (4) Performing spatiotemporal matching processing on the risk level interval map and the patient's geographic location information, and generating a feedback data packet using an asymmetric encryption algorithm; (5) Transmit the feedback data packet to the medical terminal and the patient terminal through the Internet of Things communication protocol to generate graded early warning prompt information; (6) Write the graded warning information and corresponding assessment basis data into the medical record database to form a dynamic monitoring record.

[0044] Specifically, the grading module 106 processes the key indicators in the real-time myocardial injury risk warning data. For the troponin indicator, the current test value is read and compared with the standard threshold in the reference value database, and the excess multiple and duration are calculated; for the creatine kinase indicator, the proportion and total activity of its MB subtype are analyzed to obtain the corresponding risk score. The standard threshold range under different pathological conditions is stored in the marker reference value database, and the specific risk score of each indicator is obtained by comparison and analysis. The correlation analysis between the risk score and the patient's medical history data adopts a multidimensional data fusion method. The patient's previous cardiovascular disease history, including the number of onsets, severity, treatment plan and other information, is extracted from the medical record database, and this information is arranged in chronological order to form a medical history time series. The multidimensional association calculation matches the current risk score with the key events in the medical history time series, and calculates the weight of each risk factor. The weight calculation takes into account multiple dimensions such as disease recurrence frequency, treatment effect, and complications, and finally generates a comprehensive risk score containing complete time series characteristics.

[0045] The European Society of Cardiology guidelines were used to divide the intervals for the comprehensive risk score. The risk level was divided into three levels according to the total ischemic time of the heart: ischemic time within 1 hour was mild risk, 1-2 hours was moderate risk, and more than 2 hours was severe risk. Within each risk level, it was subdivided into multiple sublevels according to the changing trend of biomarker levels. Through this multi-level grading method, a refined hazard level interval map was established. The hazard level interval map was matched with the patient's geographic location information using spatiotemporal data association technology. The patient's real-time location coordinates were obtained through GPS positioning, and the patient's activity pattern was analyzed in combination with mobile trajectory data. The location information and risk level data were superimposed in the spatiotemporal dimension to form a risk distribution map with geographic attributes. The integrated data was encrypted using the RSA asymmetric encryption algorithm to generate a feedback data packet containing encrypted data and verification information.

[0046] The feedback data packet is transmitted through the IoT communication protocol and supports multiple communication methods such as WiFi, NFC or GPS. At the medical terminal, the data packet is decrypted to display detailed risk assessment results, including the specific values ​​of each indicator, the basis for determining the risk level and the recommended measures. At the patient terminal, it is converted into easy-to-understand early warning prompt information, such as risk level prompts, medical advice, etc. The complete early warning information and its evaluation basis are written into the medical record database. The evaluation basis includes biomarker detection data, clinical history records, geographic location information, etc. These data are stored in a standard format to form a dynamic monitoring record of the patient. Each record contains information such as timestamp, data source, processing flow, etc., which is convenient for subsequent disease tracking and analysis.

[0047] For example: The real-time monitoring data of a patient showed that the concentration of troponin continued to rise, exceeding the upper limit of normal by 6 times, and the proportion of creatine kinase MB subtype reached 8%. The patient's medical history was found to have occurred twice within a year and had a history of hypertension. After comprehensive analysis of these indicators, a risk score was generated, and the ischemic time threshold was used to determine the risk as moderate. The patient is currently within 5 kilometers of his residence. This information is encrypted together with the risk level and transmitted to the medical terminal. After receiving the warning information, the doctor recommends that the patient seek medical treatment immediately. At the same time, the data of the entire monitoring and warning process is stored in the medical record database for subsequent follow-up analysis.

[0048] In a specific embodiment, if Figure 2 As shown, it is a structural schematic diagram of the grading module 106 in the early myocardial injury monitoring system based on the medical Internet of Things in an embodiment of the present application. The grading module 106 includes: The sampling unit 1061 is used to perform time series sampling on the comprehensive risk score to obtain risk score data at different time points; A calculation unit 1062 is used to perform cumulative ischemia time calculation on the risk score data to generate ischemia duration data; The grading unit 1063 is used to set a risk grading threshold point based on the ischemia duration data, wherein within one hour is a mild risk, one to two hours is a moderate risk, and more than two hours is a severe risk; An extraction unit 1064 is used to extract features for different risk level intervals to form a risk level feature vector; The comparison unit 1065 is used to compare and analyze the risk level feature vector with the current monitoring data in real time to determine the interval to which the current risk belongs; The updating unit 1066 is used to dynamically update the determined risk interval and generate a danger level interval map.

[0049] Specifically, the sampling unit 1061 in the grading module 106 first obtains the time series data of the comprehensive risk score. Using fixed time interval sampling, the risk score is recorded every 5 minutes to form discrete time series data points. Each sampling point contains a timestamp, a risk score value, and the corresponding biomarker data. Through this timed sampling method, continuous monitoring of the patient's status is ensured. The calculation unit 1062 calculates the duration of the biomarker level exceeding the standard based on the risk score data obtained by sampling. The cumulative timing starts from the time point when the normal value is first exceeded, and the trend of the marker concentration change in each time period is recorded. The calculation of the duration of ischemia takes into account the fluctuation of the marker concentration. When the concentration falls back to the normal range, the timing is suspended, and when it exceeds the standard again, it continues to accumulate, thereby accurately reflecting the actual duration of myocardial ischemia.

[0050] The grading unit 1063 sets the risk grading threshold according to the European Society of Cardiology guidelines. Taking the duration of ischemia as the main grading basis and combining it with the marker concentration level, the risk level is divided into three intervals: within one hour is a mild risk, indicating the need for close observation; one to two hours is a moderate risk, and timely medical treatment is recommended; more than two hours is a severe risk, requiring emergency treatment. Each risk level is also subdivided into multiple sub-levels to achieve a more refined risk division. The extraction unit 1064 extracts data features from different risk level intervals. Four main features are extracted: time features (duration, attack frequency), concentration features (peak value, rate of change), symptom features (accompanied manifestations) and medical history features (previous attacks). These features are organized into vector form, and each risk level corresponds to a feature vector, describing the typical feature pattern of that level.

[0051] The comparison unit 1065 compares the currently monitored data with the feature vector in real time. The pattern matching algorithm is used to calculate the similarity between the current data and the feature vectors of each risk level. The similarity calculation comprehensively considers the weights of each feature and classifies the current state into the most matching risk interval. The comparison process is carried out in real time to ensure the timeliness of risk level determination. The update unit 1066 is responsible for the dynamic maintenance of the hazard level interval map. As new monitoring data is continuously generated, the feature vectors of each risk level are regularly updated. The update process includes recalculation of feature values, dynamic adjustment of interval boundaries, and identification and processing of abnormal patterns. Through this dynamic update mechanism, the accuracy of the risk assessment standard is maintained.

[0052] For example: When monitoring a patient at high risk of myocardial infarction, the sampling unit records the levels of troponin and creatine kinase every 5 minutes. When it is found that troponin begins to rise, the calculation unit starts the timer to record the duration of the excess. After 30 minutes, the concentration continues to rise and chest pain symptoms appear, and the grading unit classifies it as a mild risk. The extraction unit analyzes the data features and finds that it is similar to the attack pattern recorded in the previous medical history. The comparison unit compares the current state with the feature library to confirm the accuracy of the risk level judgment. At 1 hour and 15 minutes, due to the worsening symptoms and the continued increase of markers, the update unit raises the risk level to moderate and updates the patient's risk feature model.

[0053] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An early myocardial injury monitoring system based on medical Internet of Things, characterized in that: The early myocardial injury monitoring system based on the medical Internet of Things includes: The quality control module is used to perform antigen-antibody reaction processing on peripheral blood samples by immunochromatography, and perform optical density analysis according to the color development degree of the test line and the quality control line to obtain quantitative data of myocardial injury biomarkers; An extraction module is used to perform structured extraction of the patient's basic characteristic information and clinical history information according to a preset information collection template, and to associate and integrate the information with the quantitative data of myocardial injury biomarkers to obtain an initial myocardial injury monitoring data set; An encryption module is used to encrypt the initial myocardial injury monitoring data set according to an asymmetric encryption algorithm, generate an encrypted data packet through a data compression algorithm, transmit and decrypt and decompress it through the Internet of Things protocol, and obtain the original myocardial injury monitoring data; The classification module is used to label and classify the original myocardial injury monitoring data through a hierarchical clustering algorithm, and to perform data calibration and standardization processing in combination with reference standard values ​​in a multi-source database to form a myocardial injury analysis data set; The evaluation module is used to perform multi-parameter risk assessment on the myocardial injury analysis data set based on the deep learning model, perform spatiotemporal correlation analysis in combination with geographic location information, and generate real-time myocardial injury risk warning data; The grading module is used to grade the real-time myocardial injury risk warning data according to the warning level assessment rules, feed back to the terminal through data encryption transmission, and store the assessment results in the database to form dynamic monitoring records.

2. The early myocardial injury monitoring system based on medical Internet of Things according to claim 1 is characterized in that: The quality control module is used to: The peripheral blood sample is chromatographically migrated through the capillary effect to form an immune response flow; The immune reaction flow is captured and enriched according to the specific binding of antigen and antibody to generate a color reaction strip; Based on the colorimetric method, the optical density of the color reaction strip is measured to obtain the gray value of the strip; The grayscale value of the strip is numerically standardized to obtain the standard optical density value; The standard optical density value is converted into concentration through standard curve calculation to form concentration data; The myocardial injury biomarkers are quantitatively analyzed based on the concentration data to obtain quantitative data of the myocardial injury biomarkers.

3. The early myocardial injury monitoring system based on medical Internet of Things according to claim 1 is characterized in that: The extraction module is used to: The basic feature information is converted into key-value pairs through field mapping to obtain structured basic feature data; The clinical history information is converted into time series records through time series coding to obtain structured clinical history data; Timestamps are timestamped and merged between structured basic feature data and structured clinical history data to generate time series feature data; Through data verification, missing values ​​in time series feature data are supplemented to obtain complete feature data; Align the complete feature data and the quantitative data of myocardial injury biomarkers to form associated feature data; The associated feature data are merged by data identification to obtain the initial myocardial injury monitoring data set.

4. The early myocardial injury monitoring system based on medical Internet of Things according to claim 1 is characterized in that: The encryption module is used for: Acquire quantitative data of myocardial injury biomarkers and clinical information data of patients in the initial myocardial injury monitoring data set; Inputting the quantitative data of myocardial injury biomarkers and the clinical information data of the patient into the data encryption management model, generating a segmented encryption scheme matching the myocardial injury monitoring, wherein the segmented encryption scheme includes a biomarker data encryption segment, a clinical information data encryption segment, and an overall encryption path composed of a plurality of encryption segments, and setting corresponding data verification parameters for each data encryption segment; Perform RSA encryption on each data encryption segment and verify the encryption validity based on data verification parameters; The encrypted data is compressed in blocks to generate an encrypted data packet with a check bit, wherein the encrypted data packet includes encryption position information of the data encryption segment, encryption parameter information and data check information; The encrypted data packets are transmitted in blocks through the network protocol, and the data integrity is checked at the receiving end; The encrypted data packet is decrypted according to the encrypted position information and the encrypted parameter information to obtain the original myocardial injury monitoring data.

5. The early myocardial injury monitoring system based on medical Internet of Things according to claim 1 is characterized in that: The classification module is used to: Obtaining biomarker quantitative data and medical history characteristic data from original myocardial injury monitoring data; The biomarker quantitative data are clustered and stratified according to the concentration value range to generate multi-level concentration distribution data, wherein the multi-level concentration distribution data includes the baseline concentration value, change trend value, peak point and corresponding time series information of the biomarker; Compare and analyze the multi-level concentration distribution data with the reference values ​​of myocardial injury markers, cardiovascular disease data, and medical record data in the multi-source database to establish a grading standard for the severity of myocardial injury; The concentration distribution data is labeled based on the myocardial injury severity grading standard to generate graded data containing risk level labels; Perform data calibration on abnormal numerical points in the classification data to eliminate data deviations caused by interference factors; The calibrated classification data were correlated with the medical history feature data to form a myocardial injury analysis data set.

6. The early myocardial injury monitoring system based on medical Internet of Things according to claim 1 is characterized in that: The evaluation module is used to: The difference between the biomarker concentration values ​​in the myocardial injury analysis data set and the reference standard range is calculated to obtain concentration deviation degree data; The concentration deviation degree data is temporally correlated with the medical history characteristic data, and a risk trend curve is generated according to the time nodes; Calculate the slope and analyze the inflection point of the risk trend curve to identify the time point when myocardial damage worsens; Map the time point of myocardial injury aggravation with geographic location information to establish a correlation map between the patient's activity trajectory and the degree of myocardial injury; Conduct risk factor attribution analysis on the association map and calculate the contribution weight of each risk factor; The contribution weight and current monitoring data are weightedly calculated to generate real-time myocardial injury risk warning data.

7. The early myocardial injury monitoring system based on medical Internet of Things according to claim 1 is characterized in that: The grading module is used to: Analyze the troponin and creatine kinase indicators in the real-time myocardial injury risk warning data, compare them with the standard thresholds in the myocardial injury marker reference value database, and obtain the risk level scores of each indicator; Based on the risk score and the patient's previous cardiovascular disease history and medical history time series, a multi-dimensional correlation calculation is performed to generate a comprehensive risk score that includes time series characteristics; The comprehensive risk score is divided into intervals according to the total cardiac ischemia time threshold, and a risk level interval map including mild, moderate and severe ischemia is established; Perform spatiotemporal matching processing on the risk level interval map and the patient's geographic location information, and generate a feedback data packet through an asymmetric encryption algorithm; Transmit feedback data packets to medical terminals and patient terminals through the Internet of Things communication protocol to generate graded early warning prompt information; The graded warning prompt information and the corresponding evaluation basis data are written into the medical record database to form a dynamic monitoring record.

8. The early myocardial injury monitoring system based on medical Internet of Things according to claim 7 is characterized in that: The grading module comprises: The sampling unit is used to perform time series sampling on the comprehensive risk score to obtain risk score data at different time points; A calculation unit, used for performing cumulative calculation of ischemia time on the risk score data to generate ischemia duration data; A grading unit is used to set risk grading threshold points based on ischemia duration data, wherein one hour is a mild risk, one to two hours is a moderate risk, and more than two hours is a severe risk; An extraction unit, used for extracting features for different risk level intervals to form a risk level feature vector; The comparison unit is used to compare and analyze the risk level feature vector with the current monitoring data in real time to determine the interval to which the current risk belongs; The updating unit is used to dynamically update the determined risk interval and generate a hazard level interval map.

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