536 results about "Assay technique" patented technology

The invention relates to the technical field of medical data analysis, in particular to a lung cancer dynamic evaluation method and system based on immune monitoring, and the method comprises the following steps: collecting immune index data obtained in the immune monitoring process of a patient, analyzing the mean value change between adjacent segments of an immune index data time sequence, marking an abnormal fluctuation region, and calculating the immune index data; and generating abnormal fluctuation section data. According to the method, the immune monitoring data of the patient are divided into the time sequences, the abnormal fluctuation sections and the abnormal change points are accurately extracted through the mean value change analysis and sliding window method, the remarkable fluctuation of the immune system can be captured more rapidly and accurately, and therefore the timeliness of dynamic monitoring is improved. And further in combination with statistical feature analysis and similarity matrix calculation, differentiated grouping of immune indexes among patients is refined, and personalized evaluation for patient groups with different immune states is facilitated.

The invention discloses a clinical examination and detection item correlation analysis method based on multi-agent cooperation, and relates to the technical field of correlation analysis, and the method comprises the steps: obtaining the name and basic parameters of an examination item, and determining the technical field of the examination item through a classification system and an algorithm; related field parameters are extracted from the knowledge management module, and configuration parameters of the intelligent agent are set; generating a task instruction based on the basic parameters, transmitting the instruction through a structured message transmission mechanism, and obtaining an agent processing result; and performing verification analysis on the processing result by using a hypothesis production and verification engine to obtain a correlation analysis result of the project. According to the method, the hypothesis content can be evaluated from multiple angles, it is ensured that the obtained correlation analysis result has high scientificity and credibility, and powerful support and basis are provided for research and application of clinical examination and detection items.

The invention relates to the field of bioinformatics and drug development, and discloses an efficient siRNA effectiveness prediction method and system based on an XGBOOST algorithm. The method comprises the following steps: acquiring a training data set containing siRNA sequence information, chemical modification information and corresponding validity data; carrying out cleaning, feature extraction and data balance processing on the data; training the prediction model by using an XGBOOST algorithm, and optimizing hyper-parameters; performing model evaluation and optimization through cross validation; a new siRNA sequence is input for effectiveness prediction; explaining a prediction result by using an SHAP value analysis technology; the actual interference efficiency of the efficient siRNA sequence is verified through an in-vitro cell experiment, and the model is further optimized based on an experiment result. According to the method, the accuracy and efficiency of siRNA effectiveness prediction are improved, the influence of chemical modification is comprehensively considered, the problem of data imbalance is solved, the model interpretation is enhanced, and the calculation efficiency is remarkably improved.

The invention discloses a mutant protein screening method and device and related equipment thereof, and relates to the technical field of material determination and analysis. The method comprises the following steps: respectively carrying out simulated single mutation on a plurality of amino acid sites in a protein sequence of a target protein to obtain a plurality of simulated mutant proteins, and determining adaptability evaluation values F of the plurality of simulated mutant proteins based on a target protein language model; determining structural stability values S of the plurality of simulated mutant proteins based on a protein folding energy calculation tool; performing normalization processing on the adaptability evaluation value F and the structural stability value S, and performing single-index sorting and comprehensive-index sorting on normalization processing results; and screening the plurality of simulated mutant proteins according to the target screening number, the single index sorting result and the comprehensive index sorting result to obtain a target mutant protein. According to the method, the protein design transformation difficulty can be reduced, the mutant protein screening effect is improved, the calculation amount required by protein transformation and screening is reduced, and the consumption cost is reduced.

The invention relates to the technical field of prognosis analysis, in particular to a pancreatic cancer prognosis analysis system based on big data mining, which comprises a jump trend extraction module, an index grade mapping module, a sample stability screening module, a path node offset identification module and a prognosis stage classification module. According to the method, a change rate difference value and a third-order response sequence are constructed for continuous three-stage pancreatic cancer biological index sequences, jump trend feature points are extracted, and a risk grade transition sequence is formed in combination with grade span and direction judgment; multi-dimensional normalized parameters of a variable coefficient, a survival score difference value and an organ function score standard deviation are introduced to perform stable sample screening, interference of data disturbance on a path judgment result is reduced, and path mutation node positions are screened in combination with a path stability threshold value; precise classification and marking of high-risk variation stages in prognosis paths of pancreatic cancer patients are realized, stage reference is provided for individualized intervention strategies, and risk identification sensitivity and layered intervention scientificity are improved.

The invention belongs to the technical field of histopathology full-slice image analysis, and relates to a gene mutation prediction method and system based on a pathological report and full-slice cross-modal representation learning, and the method comprises the steps: obtaining a histopathology full-slice image and a data set of a corresponding pathological and molecular detection report, and carrying out the preprocessing and coding; performing word segmentation and feature extraction on the pathological diagnosis report and the molecular detection report; cross-modal representation learning is realized through reconstruction of pathological report features and comparative learning based on a category refinement queue; designing a loss function and training the model offline by using a sample data set with a label; and performing lung cancer gene mutation prediction on the histopathology full-slice image by using the trained model. According to the method, the defects that detailed annotations of histopathology full-slice image data are difficult to obtain and the single-modal information expression ability is insufficient can be overcome, and slice image level mutation tag classification is effectively completed by using a cross-modal information guide weak supervision method of a pathological report and a molecular detection report.

The invention belongs to the technical field of biological detection, and particularly relates to a disease marker fluorescence instant detection method based on protein scaffold DNA-Cu2 + functional nanospheres, which comprises the following steps: combining an insulin-like growth factor 1 (IGF-1) aptamer or cortisol aptamer, protein-DNA and DNA-metal ions; the protein scaffold DNA-Cu < 2 + > multifunctional integrated nanospheres are synthesized by a one-step method at room temperature. Based on the function integration of the nanosphere, one-step detection of IGF-1 or cortisol is realized, a plurality of analysis modes including fluorescence detection and smartphone-RGB are provided by combining a test strip and smartphone red, green and blue (RGB) analysis technologies, and a rapid and sensitive disease marker instant detection method is constructed. Finally, rapid and convenient detection of multiple markers in hundreds of clinical samples of multiple diseases is realized, and a new solution is provided for paper-based fluorescence POCT.

The invention discloses a method, a sensor and a system for testing an analyte in a biological sample, and belongs to the technical field of biological sample analysis. The method comprises the following steps: contacting a first working electrode and a second working electrode with a biological sample, and inputting a first excitation signal and a second excitation signal to the first working electrode; inputting an excitation signal sequence to the second working electrode between the first excitation signal and the second excitation signal; measuring at least one first output signal responsive to the first excitation signal, at least one second output signal responsive to the second excitation signal, and at least one sequence of output signals responsive to the sequence of excitation signals on the first working electrode; and obtaining the concentration of the analyte in the biological sample according to the first output signal, the second output signal and the output signal sequence. The first output signal, the second output signal and the output signal sequence are used as parameters for calculating the concentration of the analyte in the biological sample, so that the influence of interferents can be eliminated, and the detection accuracy of the test method is improved.

The invention relates to the technical field of biomedical data analysis, and discloses a data analysis method and system for genetic disease gene detection and a storage medium, and the method comprises the following steps: collecting a patient sample, and carrying out high-throughput sequencing to obtain original data; performing quality control and comparison processing on the data to generate variation detection data, and calculating variation information amount; suspicious variation sites are automatically screened, variation information amount is analyzed based on information entropy, and key variation is screened according to a preset threshold; performing Bayesian inference analysis on the key variation, calculating pathogenicity probability, and performing pathogenicity judgment based on an ACMG standard; calculating the matching degree of the key variation and the phenotype in combination with the phenotype information of the patient, and screening the variation conforming to phenotype characteristics; submitting the screening result to a doctor for auditing, and generating a final screening result; and generating a standardized gene detection report according to a final result. According to the method, pathogenic variation is efficiently and accurately recognized, and the automation level and clinical application value of genetic disease gene detection are improved.

The invention provides a premature delivery prediction method and device, electronic equipment and a readable storage medium, and relates to the technical field of data analysis. The method comprises the following steps: based on a preset model, scoring acquired comprehensive information of a pregnant woman in an early pregnancy stage; wherein the comprehensive information comprises demographic information, clinical information and biochemical index information; and predicting the premature delivery risk of the pregnant woman according to the scoring result. According to the method, the comprehensive information of the pregnant women at the early pregnancy stage is scored through the preset model, the premature delivery risk of the pregnant women is predicted according to the scoring result, quantitative evaluation of premature delivery risk prediction of the pregnant women at the early pregnancy stage is achieved, and objectivity and certain accuracy are achieved; and the difficulty and the cost of obtaining the comprehensive information of the pregnant women in the early pregnancy are relatively low, the difficulty and the cost of predicting the premature delivery risk of the pregnant women in the early pregnancy are reduced, and medical resources are saved.

The present invention discloses a disease prediction method and system based on adaptive fusion of microbiome hierarchical features, which relates to the technical field of microbiome data analysis. A trained disease prediction model is used to predict diseases based on microbiome data. The disease prediction model performs multi-level feature extraction on feature data of five classification levels through a hierarchical feature extraction module, and integrates the results into multi-level fusion features. The multi-scale feature fusion module processes the multi-level fusion features through three parallel channels of pattern flow, context flow and content flow to achieve an all-round representation of microbiome data. The dynamic sampling module is introduced in the training process, and based on the class balance mechanism and the uncertainty evaluation strategy, the sample sampling weights are dynamically adjusted. Finally, a classifier is used to predict diseases for the multi-scale fusion features. The present invention makes full use of microbiome classification level information, realizes multi-dimensional feature representation, effectively addresses the problem of sample imbalance, and improves the accuracy of disease prediction.

The invention provides a breast cancer HER2 immunohistochemical digital slice automatic interpretation method, device and equipment and a storage medium. Relates to the technical field of digital pathological image analysis. Acquiring a full-slice image, automatically identifying an external contrast area and a tissue area through a multi-task head real-time instance segmentation model, and segmenting an ROI (Region of Interest) area of tumor cell aggregation; extracting image blocks by adopting a fixed sliding window based on the ROI region, and constructing a double-branch multi-task feature extraction model containing a backbone network: realizing HER2 expression pattern classification by an image block classification head, and outputting a continuous protein expression intensity quantized value by a dyeing intensity numerical head through regression analysis; and establishing a slice-level interpretation model, integrating the classification results and the intensity quantized values of all the image blocks, and outputting an automatic HER2 interpretation result. According to the invention, end-to-end analysis from cell-level characteristic quantification to slice-level diagnosis is realized, and an objective and quantifiable intelligent interpretation scheme is provided for HER2 immunohistochemical evaluation.

The invention relates to the technical field of Escherichia coli drug resistance analysis, in particular to an Escherichia coli antibiotic drug resistance detection method based on SERS and machine learning. The invention provides an Escherichia coli antibiotic resistance detection method based on SERS (Surface Enhanced Raman Scattering) and machine learning so as to overcome the defect that high-precision and rapid diagnosis cannot be realized in the prior art. The invention aims to realize rapid and high-precision identification of drug-resistant bacteria through a machine learning algorithm by utilizing a core-shell nanostructure SERS substrate of silver-coated gold nanoparticles and combining SERS spectrum differences of drug-resistant escherichia coli with different phenotypes. Through the method, the detection time can be remarkably shortened, the detection precision can be improved, an accurate drug treatment scheme is provided for clinic, and the method has a wide application prospect.

The invention relates to the technical field of pharmaceutical analysis, and particularly provides a characteristic chromatogram of epimedium villosum, a characteristic chromatogram construction method and an identification method. According to the method, an epimedium villosum medicinal material is pretreated, and the characteristic chromatogram of the epimedium villosum is identified by means of a chromatographic analysis technology; the epimedium villosum and the planted epimedium villosum are distinguished through the relative peak area and the characteristic peak of the chromatographic peak, so that the technical problem that the wild epimedium villosum and the planted epimedium villosum cannot be distinguished by the existing methods such as appearance identification, thin-layer identification and DNA identification is solved, and the correct source of the wild epimedium villosum is effectively ensured; the method has the advantages of simple identification method and accurate result.

The invention relates to the technical field of detection and analysis, and discloses a nanoparticle for detecting the concentration of methotrexate as well as a preparation method and application of the nanoparticle. The nanoparticles disclosed by the invention comprise hairpin DNA which is connected with the gold nanoparticles and is provided with a marking group and aptamer chain DNA which can be specifically recognized by methotrexate, and the aptamer chain DNA and walking chain DNA are hybridized into double-chain DNA which is connected to a magnetic bead, on one hand, the concentration of MTX is measured by marking a DNA chain of a fluorophore and collecting a fluorescence signal; on the other hand, after sodium chloride is added into the gold nanoparticles without DNA protection, the color of the gold nanoparticles changes under a high-salt condition, and the concentration of MTX is measured by collecting ultraviolet values. According to the nanoparticles provided by the invention, all parts interact with one another, signals are amplified, the effect of double detection of MTX concentration is exerted, and a powerful strategy is provided for accurately mastering the drug concentration in real time and improving the treatment safety and effect.

The invention provides a method for detecting a virus inactivation effect by an ELISA (Enzyme-Linked Immunosorbent Assay) method, which comprises the following steps: acquiring a to-be-detected inactivated virus sample, extracting an antigen component from the sample, and detecting an antigen structure change characteristic value through a high-throughput mass spectrometry analysis technology to obtain antigen degradation degree data; obtaining a sample subjected to preliminary inactivation, and detecting the antigen content change trend through an enzyme-linked immunosorbent assay to obtain content change curve data; detecting the residual quantity of viral nucleic acid by a real-time fluorescent quantitative PCR (Polymerase Chain Reaction) technology aiming at the immunogenicity retained sample to obtain nucleic acid degradation degree data; acquiring nucleic acid degradation degree data, classifying the relationship between nucleic acid residues and infectivity by adopting a support vector machine algorithm, and judging that the inactivation effect is completely completed if the classification result shows that the infectivity is lost; according to the antigen content change trend, the immunogenicity index and the nucleic acid degradation degree data, a weighted fusion algorithm is adopted to integrate multi-dimensional detection results, and a comprehensive inactivation effect score is obtained.

The invention relates to the technical field of liver cancer analysis, in particular to a liver cancer analysis method, system and equipment based on multi-modal deep learning and a medium. The method comprises the following steps: acquiring pathological data, transcriptome data, methylation data and clinical data of a target patient, and respectively preprocessing to obtain target pathological data, target transcriptome data, target methylation data and target clinical data; respectively carrying out feature extraction to obtain pathological features, transcriptome features, methylation features and clinical features; performing feature fusion on the pathological features, the transcriptome features, the methylation features and the clinical features to obtain liver cancer analysis features; extracting lipid metabolism related marker characteristics of the target patient, and performing liver cancer comprehensive analysis on the target patient according to the lipid metabolism related marker characteristics and the liver cancer analysis characteristics. According to the application, the tumor characteristics of the target patient can be comprehensively reflected, the accuracy of auxiliary diagnosis is improved, and accurate prognosis prediction is provided for the target patient.

The invention relates to the technical field of fluorescence immunoassay, and discloses a quantum resonance fluorescence immunoassay medical detection method, which comprises the following steps: 1, carrying out fixation and permeation treatment on cells, respectively incubating the cells by using labeled different quantum dot-antibody compounds, specifically binding antibodies with corresponding target proteins in the cells, and detecting the target proteins in the cells; a labeled cell sample is obtained; 2, under a confocal microscope, laser with a specific wavelength is adopted to excite the marked cell sample, and fluorescence lifetime image data of different quantum dot emission wavelengths are collected; according to the method, the fluorescence lifetime image data of different quantum dot emission wavelengths are acquired through the confocal microscope, the position and content information of different target proteins can be clearly distinguished, simultaneous analysis and accurate positioning of multiple target proteins are realized, and an efficient means is provided for multi-protein research in a complex biological system.

The invention discloses a urine sampling device for the endocrinology department and a sampling method, and belongs to the technical field of medical analysis. Comprising the following modules: an automatic sampling control module for realizing automatic control of the whole urine sampling process, ensuring that sampling accords with a standard process, and supporting automatic sub-packaging and storage management of samples; the sample quality control module is used for monitoring the physical characteristics of the samples in real time, automatically evaluating the effectiveness of the samples in combination with environmental parameter monitoring, and identifying and marking abnormal samples; the data acquisition and processing module is used for comprehensively acquiring physiological data, nutrition metabolism data, operation process data and sample tracing information, and performing cleaning, standardization and fusion processing to generate a standardized data set; the intelligent diagnosis and analysis module is used for intelligently detecting abnormal physiological indexes and carrying out disease risk prediction and early warning; and the man-machine interaction module provides a visual operation interface and integrates multi-level user authority management, automatic report generation and remote monitoring.

The invention discloses a digital single-molecule enzyme multi-activity characteristic cross-scale heterogeneity dynamic evaluation method, which comprises the following steps: diluting a pre-combined enzyme-template-primer ternary complex to a single-molecule level so as to load the enzyme-template-primer ternary complex into a reaction chamber in a micropore array chip for rolling circle amplification, a molecular beacon is combined with an amplification product to release a fluorescence signal; monitoring and acquiring the fluorescence signal in real time to generate a time-resolved fluorescence intensity curve; extracting a slope parameter of the fluorescence intensity curve so as to obtain a catalytic synthesis rate through conversion calculation for evaluating enzyme activity; the distribution of catalytic synthesis rate values is fitted using an n-fold Gaussian mixture model to obtain a standard deviation for assessing population homogeneity and a coefficient of variation for assessing stability. According to the invention, cross-scale analysis of single-molecule resolution, multi-parameter dynamic association and environmental interference processing can be realized at the same time, and resolution limitation of a traditional analysis technology is broken through by integrating single-molecule fluorescence tracking and population statistics.

The invention belongs to the technical field of tumor clustering analysis, and particularly relates to a tumor clustering analysis system and method based on big data. Through deep fusion and space-time alignment processing of multi-dimensional biological data, the limitation of single-dimension or simple data integration in a traditional method is overcome, complex biological characteristics and space-time evolution laws in the tumor can be captured more comprehensively, and the tumor can be rapidly and accurately captured by constructing a low-dimensional association and independent characteristic space and combining a nonlinear dimension reduction technology. According to the method, low-dimensional feature vectors with biomarker features are effectively extracted, a high-quality data basis is provided for subsequent clustering analysis, meanwhile, based on sliding window density estimation and determination of a dynamic neighborhood radius, accurate spatial adjacency relation construction of the low-dimensional feature vectors is achieved, and the accuracy of the spatial adjacency relation is improved. And the potential clustering center of the tumor subtype is extracted through the multi-scale characteristic spectrum, so that the generated classification label can more accurately reflect the real distribution of the tumor subtype.

The invention relates to the technical field of medical data analysis, in particular to a lung adenocarcinoma osimertinib drug resistance prediction model construction method and system based on machine learning. Clinical data of lung adenocarcinoma patients and mRNA (messenger ribonucleic acid) data of a public database are integrated, after Mann-Wh itney U inspection, FPKM standardization and other preprocessing are carried out, XGBoost is utilized to construct a basic model driven by clinical indexes, a prognosis model driven by gene characteristics is constructed through R packet Mime, and TYMS and UAP1L1 are screened out to serve as key genes. The performance of the model is optimized through residual correction and stacking integration, and finally the AUC is improved to 0.924. Serum RNA (Ribonucleic Acid) detection and verification show that TYMS and UAP1L1 are remarkably and highly expressed in a drug-resistant group. According to the method, traditional biopsy is replaced with noninvasive serum detection, the detection cost is reduced, the model generalization ability is verified through an independent queue, and an efficient and accurate solution is provided for early warning and personalized treatment of osimertinib drug resistance of the lung adenocarcinoma patient.

The invention discloses a component detection system for unmanned automatic production of an extracting solution, and relates to the technical field of automatic production and quality control. The system integrates a plurality of detection technologies, including infrared spectroscopic analysis, OD value detection, pH value monitoring and absorbance detection, so as to realize real-time monitoring and automatic production management of the components of the extracting solution. By establishing a standard product fingerprint spectrum database, the system can automatically compare real-time detection data with a standard spectrum, and immediately triggers an alarm or stops production once an abnormality is found, so that the stability of product quality and the high efficiency of a production process are ensured. The system not only improves the automation degree of a production line, but also predicts and optimizes bioactive components through a whole genome nutrition group analysis technology in combination with species genome, transcriptome data and metabolome information, realizes precise formula and efficient production, and provides powerful support for an unattended automatic production system.

Systems and methods are disclosed for analyzing data from oncology treatments such as immune checkpoint inhibitor or radiotherapy therapies, including predicting adverse events of the oncology therapies, predicting objective response of the oncology therapies, predicting symptoms from the oncology therapies, and use of such predictions by technological implementations to achieve improved system and medical outcomes. An example technique for generating a predicted treatment outcome includes: receiving patient data for a human subject, which provides patient-reported outcomes collected from the human subject relating to a particular oncology treatment; processing the patient data with a trained artificial intelligence (AI) prediction model, which receives the patient data as input and produces a prediction of a treatment outcome as output; and outputting data to modify a treatment workflow of an oncology treatment for the human subject, based on the prediction of the treatment outcome.

The invention belongs to the technical field of synthetic biology and biological detection and analysis, and particularly relates to a trimethylamine N-oxide biosensor, a genetically engineered bacterium and application of the genetically engineered bacterium. Specifically, a TMAO response type biosensor based on a TorRST atypical two-component system (TCS) is developed in escherichia coli, and the TMAO response type biosensor is optimized. Meanwhile, a TMAO degradation pathway regulated and controlled by a TMAO biosensor is introduced into escherichia coli EcN, so that an engineering probiotic capable of sensitively detecting, regulating and controlling degradation and balance of TMAO is successfully developed. Therefore, the compound can be used for preventing and treating diseases caused by TMAO, and has good practical application value and prospect.

The invention relates to the technical field of online detection and analysis, and discloses a bone joint synovial fluid detection and analysis system based on microfluidics, which specifically comprises the following steps: acquiring synovial fluid rheological data and synovial fluid molecular activity data in a bone joint movement process and cartilage surface morphological data in a joint buckling process through a biosensor; constructing a synovial fluid dynamic anomaly analysis model, and performing synovial fluid function anomaly analysis through the synovial fluid rheological data and the molecular activity data; a cartilage stress anomaly analysis model is constructed, and cartilage stress anomaly analysis is carried out through cartilage surface morphological change data; obtaining a synovial fluid function abnormality analysis result and a cartilage stress abnormality analysis result to carry out bone joint function adaptability analysis; and acquiring a bone joint function adaptability analysis result and carrying out synovial fluid function abnormality analysis early warning. The problem that in the prior art, due to the fact that the bionic degree of a detection strategy is insufficient and bone joint dynamic parameters are deficient, the association degree of a static synovial fluid detection result and the actual joint rehabilitation condition of a patient is poor is solved.

The invention discloses a trypsinogen-2 detection method and kit based on a homogeneous light-excited chemiluminescence immunoassay technology, and belongs to the technical field of immunodetection analysis. The method provided by the invention comprises the following steps: preparing donor microspheres and receptor microspheres coupled with a trypsinogen-2 capture antibody, adding the coupled donor microspheres and receptor microspheres into a sample to be detected, uniformly mixing and incubating without cleaning, and finally detecting by using a fluorescence detector. The kit comprises donor microspheres coupled with a trypsinogen-2 capture antibody, receptor microspheres coupled with a trypsinogen-2 detection antibody, a trypsinogen-2 standard substance solution and an analysis buffer solution. The trypsinogen-2 content in a sample to be detected can be rapidly detected by using the characteristics of a double-antibody sandwich immunoassay and light-activated chemiluminescence, and the trypsinogen-2 detection kit has the characteristics of wide detection range, high sensitivity, high accuracy, rapid and simple detection and the like, can be used for early auxiliary diagnosis of acute pancreatitis clinically, and has a relatively good application prospect.

The invention relates to the technical field of mass spectrometric detection and analysis, and particularly discloses an organic mass spectrometric analysis method for detecting tumor biomarkers, which comprises the following steps: S1, synthesizing gold nanoparticles; s2, sequentially assembling a CEA detection antibody modified by a connecting arm and a mass spectrum tag molecule on the surface of a gold nanoparticle through an Au-S bond to obtain a mass spectrum probe; s3, preparing the modified magnetic beads; s4, taking the modified magnetic beads as capture units, and adding a to-be-detected sample and a mass spectrum probe for immunoreaction; then the gold nanoparticles are released from the immune complex, and supernate is collected; and S5, detecting the supernate in a high-resolution mass spectrum to obtain the concentration of the antigen in the sample to be detected. When the mass spectrum tag molecule is used for organic mass spectrum analysis and tumor biomarkers are detected, a plurality of antibodies can be marked at the same time, so that simultaneous detection of multiple targets is realized; and the method has good sensitivity and detection stability.

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.

The invention relates to an iteration-based undersampling cost-sensitive ensemble classification method, belongs to the technical field of medical data analysis, and is particularly suitable for processing a disease prediction model in an unbalanced data set. According to the method, the sampling probability of the majority class samples is dynamically adjusted through an iterative undersampling strategy, it is ensured that the features of the minority class samples are focused on in the training process, and meanwhile important information of the majority class samples is prevented from being lost. In combination with cost-sensitive learning and ensemble learning, the invention provides a classification method capable of remarkably improving the minority class sample prediction performance while keeping the overall accuracy of a classifier. The method has good adaptability, is suitable for various unbalanced data set scenes, and shows an excellent application effect in medical data analysis and disease early warning.