73 results about "Disease early" patented technology

The invention discloses an intelligent animal epidemic disease monitoring management and early warning workstation, which comprises a multi-modal data acquisition module, an epidemic disease risk assessment module, a dynamic early warning threshold generation and judgment module, a visual human-computer interaction interface and a block chain storage module, the multi-modal data acquisition module is used for acquiring animal data of each monitoring point in a preset monitoring area and environment data of animals; the epidemic disease risk assessment module is used for comprehensively analyzing the animal data and the data of the environment where the animals are located to obtain epidemic disease risk indexes of all monitoring points in the current monitoring area; the dynamic early warning threshold generation module is used for dynamically generating an early warning threshold and judging an epidemic disease risk index to obtain an epidemic disease risk level and an epidemic disease risk area; the visual human-computer interaction interface is used for displaying the epidemic disease risk condition in the monitoring area; and the block chain storage module is used for data storage. According to the invention, the accuracy and timeliness of early warning of epidemic diseases are improved.

The invention relates to pattern recognition and anomaly detection in the technical field of computers, and discloses a laying hen disease early-stage AI early warning method and a laying hen disease early-stage AI early warning system. The method comprises the following steps: obtaining a laying hen behavior video stream, an excrement image and body temperature data, and carrying out preprocessing such as timestamp alignment and equipment number mapping to generate a training and reasoning input structure; then target tracking, key point coding, region segmentation, contour feature coding and body temperature fragment statistical coding are executed to extract a multi-source feature sequence; performing time window alignment, scale normalization, pattern recognition and anomaly detection processing on the multi-source features to realize disease type recognition; and finally, generating an early warning event based on rule triggering and threshold judgment, and forming closed-loop disposal through prevention and treatment scheme retrieval and instruction arrangement. According to the method, collaborative analysis and early abnormity early warning of multi-source heterogeneous data are realized, and the disease recognition accuracy and early warning timeliness are remarkably improved.

The invention relates to the field of medical treatment, in particular to application, a model and a method of a protein fragment of urine regulatory protein in preparation of a kidney disease early detection product, and the amino acid sequence of the protein fragment of the urine regulatory protein is any shown in SEQ ID NO.1-8. The model constructed according to the protein fragment of the urine regulatory protein can be used as a non-invasive tool for early detection and monitoring of kidney diseases, and by analyzing the unique mode of the protein fragment existing in a urine sample, the model which indicates renal insufficiency, renal function and renal function can be identified before the traditional clinical biomarker is abnormal. Therefore, intervention can be carried out earlier.

The invention discloses a disease early screening method based on a Stacking integrated model. Comprising the following steps: firstly, carrying out abnormal value processing by adopting a box graph theory, secondly, carrying out missing value processing by adopting a layered mean value filling method, carrying out correlation analysis and carrying out data cleaning to obtain a preprocessed data set, then, selecting several types of better base learners by applying an AUC index value, and finally, integrating the selected base learners by using a Stacking strategy, so as to obtain a data set; a new feature training set and a new test set are obtained through K-fold cross validation, and logistic regression is selected as a meta-learner for disease prediction. According to the method provided by the invention, the method is applied to experimental analysis of a pima Indiana diabetes data set, and the related evaluation indexes of the Stacking integrated model and the intermediate machine learning model are compared, so that the obtained Stacking integrated model has more excellent classification prediction performance, and can provide auxiliary decision information support for early disease screening.

The invention discloses a disease early warning method and device based on environment monitoring and a storage medium, and belongs to the field of disease prevention and control, and the method comprises the steps: collecting environment data, determining a target disease, obtaining medical data of the target disease, and determining target environment data in the environment data according to the medical data; inputting the target environment data into a prediction model corresponding to the target disease; and determining a risk level of the target disease according to an output result of the prediction model, and performing early warning prompt of the target disease according to the risk level. The invention aims to realize early warning for residents from the perspective of disease prevention and control according to the environmental data, improve the guidance of early warning for the residents to obtain which medical help, and improve the body health of the residents.

The invention relates to a disease early warning method and device, electronic equipment and a medium, and belongs to the technical field of medical decision, and the method comprises the steps: obtaining image data and physiological signal data of a patient; inputting the image data into a fully trained convolutional neural network model to obtain a first feature, and inputting the physiological signal data into a fully trained long-short-term memory network model to obtain a second feature; inputting the first feature and the second feature into a fully trained dynamic weighted graph attention network model to obtain a comprehensive feature; the comprehensive features are input into a decision model which is completely trained, a disease attack risk value is obtained, early warning is carried out based on the disease attack risk value, and the decision model is constructed based on a neural network. The structural features of the image data are extracted through the neural network, the time sequence features of the physiological signal data are extracted through the long-short-term memory network, multi-modal medical data deep fusion is achieved, and the diagnosis precision is effectively improved.

The invention belongs to the technical field of early diagnosis of diseases, and discloses a polypeptide targeting macrophage PLIN2, a PET molecular probe and a preparation method and application thereof.Firstly, the polypeptide P672-1 targeting the PLIN2 is synthesized, and 64Cu-NOTA-P672-1 is prepared on the basis of the polypeptide P672-1. The molecular probe has the good chemical yield and stability, has the good targeting performance on the PLIN2, and can be used for preparing 64Cu-NOTA-P672-1. And the biological safety performance is good. Compared with the conventional 18F-FDG, the 64Cu-NOTA-P672-1 prepared by the preparation method disclosed by the invention has a good early diagnosis effect. The preparation method of the 64Cu-NOTA-P672-1 is simple and easy to implement, and the 64Cu-NOTA-P672-1 can be used for diagnosing early abdominal metastasis of ovarian cancer with high expression of PLIN2 The nuclide 64Cu-NOTA-P672-1 disclosed by the invention has important clinical significance on early diagnosis of ovarian cancer metastasis.

The intelligent closestool comprises a closestool cover and a closestool body, the closestool body comprises a feeding cavity, a discharging channel communicated with the feeding cavity, voice interaction equipment arranged at the top of the closestool body, a master control device arranged at the bottom of the closestool body, and a power source connecting wire arranged on one side of the back face of the closestool body; the intelligent closestool has the remarkable beneficial effects in multiple aspects, a plurality of problems in the prior art are practically solved, and the defect that a traditional intelligent closestool and an existing intelligent closestool are single in function is overcome in the health monitoring aspect. The non-contact body temperature monitoring unit is used for continuously monitoring the body temperature, the intelligent sound induction and disease prediction unit is used for analyzing sound characteristics such as cough and wheeze, and the urine detection device is used for detecting urine indexes, so that the comprehensive monitoring of the health condition of the old people is realized; the problem that disease signals are difficult to perceive due to lack of key physiological index monitoring is effectively solved, and the diagnosis and treatment delay risk is greatly reduced. And the disease early warning capability is qualitatively improved.

The application provides a disease early warning method and system based on deep learning, and relates to the technical field of medical data analysis. The method comprises the following steps: acquiring an electrocardiogram signal of a patient; performing denoising processing on the electrocardiogram signal to obtain a denoised electrocardiogram signal; performing data enhancement processing on the denoised electrocardiogram signal through an SMOTE algorithm to obtain an enhanced electrocardiogram signal; constructing a disease prediction classification model based on deep learning; taking the enhanced electrocardiogram signal as input, outputting a category prediction result through the disease prediction classification model based on deep learning; and performing myocardial infarction disease early warning according to the category prediction result. In the application, the denoising processing on the electrocardiogram signal and the construction of the disease prediction classification model based on deep learning can effectively improve the data quality, automatically learn complex features, capture key time sequence correlations, and thus improve the accuracy of the prediction result.

The invention discloses a biomarker composition for predicting the biological age or aging state of an individual, and the biomarker composition comprises tetracosyl saturated fatty acid / tetracosyl monounsaturated fatty acid phosphatidylcholine (PC 24: 0 / 14: 1), cysteine-glycine-cysteine tripeptide (Cys-Gly-Cys), cysteine-glycine-cysteine tripeptide (Cys-Gly-Cys), cysteine-glycine-cysteine tripeptide (Cys-Gly-Cys), cysteine-glycine-cysteine tripeptide (Cys-Gly-Cys), cysteine-glycine-cysteine tripeptide (Cys-Gly-Cys), cysteine-glycine- The invention relates to a pharmaceutical composition for the preparation of a pharmaceutical composition, which comprises the following components in parts by weight: 20: 0 / 22: 5, 20 / docosapentaenoic acid monohexosyl sphingosine amide (HexCer 20: 0 / 22: 5), trigonelline, lithocholic acid glycine (Lithocholic acid glycine), and octadecanoic saturated fatty acid / octadecanoic monounsaturated fatty acid phosphatidylcholine (PC 18: 0 / 18: 1). The biomarker can be used for accurately characterizing the aging state of an organism, and provides a strategy for early screening, intervention evaluation, accurate health management and the like of related diseases of the elderly.

The present invention relates to a biomarker HLA-DOA for sepsis-related diseases and a use thereof. The present invention provides a use of HLA-DOA or an active fragment or functional fragment thereof in the preparation of a product for early diagnosis, risk assessment, immune status assessment, prognosis prediction and / or treatment regimen selection of sepsis-related diseases in a subject.

The invention discloses a cerebrovascular disease risk prediction method, and particularly relates to the field of disease risk prediction, and the method comprises the following steps: S1, collecting basic clinical data, dynamic physiological parameter data and image feature data of a target object, and constructing a data set; s2, performing feature extraction on the basic clinical data, the dynamic physiological parameter data and the image feature data, and fusing the extracted features to obtain a fused feature set; s3, constructing a fusion prediction model through the fusion feature set; and S4, inputting the data set into the fusion prediction model, and outputting a risk prediction result. According to the method, the information short board of a single data type is made up, different types of data are converted into a unified computable form through standardized quantitative processing, core information with risk indication significance is effectively screened out in combination with abnormal data proportion analysis and dynamic fluctuation intensity evaluation, and the recognition capability of early potential risks of diseases is remarkably improved.

The invention belongs to the field of early screening of diseases, and particularly relates to application of a protein marker in preparation of a product for early screening, identification and diagnosis of amyotrophic lateral sclerosis. The expression of LanCL1 in serum of ALS patients is higher than that in serum of healthy people and is negatively related to the state of illness, and the marker has no abnormality in Parkinson's disease patients and is high in specificity. The product is a blood detection kit, sampling is convenient, patient compliance is good, early diagnosis, identification and illness state evaluation of ALS can be achieved, and the problems that an existing marker is insufficient in specificity and complex in sampling are solved.

The present application discloses a multi-dimensional disease monitoring and early warning method, which relates to the technical field of data processing, and includes: obtaining multi-dimensional data to generate a multi-dimensional data set; establishing a historical disease source network according to the multi-dimensional data set, and obtaining existing case data to establish a real-time disease source network; comparing the real-time disease source network with multiple historical disease source networks respectively according to the hierarchical comparison principle to obtain the most similar historical disease source; marking key areas around each real-time network node according to geographic information system technology to form punctuation data; continuously monitoring the punctuation data, and defining an abnormal period and an initial onset period; preliminarily determining the disease source area and suspected disease source nodes, and cross-identifying the infection source; through the integration and weighted fusion algorithm of multi-dimensional data, the accuracy and robustness of disease early warning are improved, the pattern difference from existing diseases is accurately identified, and prevention and control measures are formulated based on the most similar historical disease source network, achieving the effects of accurate identification and targeted prevention and control.

The invention discloses a self-amplification activation type active oxygen probe as well as a preparation method and application thereof. The structural formula of the active oxygen probe is as shown in formula I in the specification. The active oxygen probe has great application potential in the aspects of disease early warning and diagnosis, accurate monitoring of biomarkers and the like. Particularly in early disease diagnosis, high-sensitivity detection is carried out on low-level active oxygen, and the diagnosis accuracy can be greatly improved, so that earlier treatment intervention is realized, and the cure rate of a patient is improved; and # imgabs0 #.

The present invention relates to a sepsis-related disease biomarker HLA-DQA and the use thereof. Provided in the present invention is the use of HLA-DQA or an active fragment and functional fragment thereof in the preparation of a product for early diagnosis, risk assessment, immune status assessment, prognosis prediction, and / or treatment regimen selection of a sepsis-related disease in a subject.

The invention provides an illness state early warning method and system based on deep learning, and relates to the technical field of medical data analysis. The method comprises the following steps: acquiring an electrocardiogram signal of a patient; performing denoising processing on the electrocardiogram signal to obtain a denoised electrocardiogram signal; based on the de-noised electrocardiogram signal, performing data enhancement processing through an SMOTE algorithm to obtain an enhanced electrocardiogram signal; constructing an illness state prediction classification model based on deep learning; taking the enhanced electrocardiogram signal as input, and outputting a category prediction result through an illness state prediction classification model based on deep learning; and performing myocardial infarction condition early warning according to the category prediction result. According to the method, the data quality can be effectively improved, complex features can be automatically learned, and key time sequence association can be captured by performing denoising processing on the electrocardiogram signals and constructing the disease condition prediction classification model based on deep learning, so that the accuracy of a prediction result is improved.

The invention discloses a three-gene combination, a primer probe set and a system for qPCR (quantitative polymerase chain reaction) rapid screening of tuberculosis progression risk and application of the three-gene combination, the primer probe set and the system. The method comprises the following steps: firstly, screening out candidate genes APOL1, C1QC and SOCS1 which are stable across time windows and have high prediction performance; then a three-gene combination scoring you model (PRI3) composed of APOL1, C1QC and SOCS1 is constructed and used for predicting the risk that a latent tuberculosis infected individual progresses into active tuberculosis within a set time window; the method comprises the following steps of: firstly, selecting a reference model (RISK6, Sweeney3, Gliddon4 and BATF2), then performing performance verification in an independent verification queue, and comparing with the existing reference model (RISK6, Sweeney3, Gliddon4 and BATF2), and the result shows that the scoring model disclosed by the invention can keep stable performance in adult population and multiple time windows (such as 0-6 months, 0-12 months and 0-24 months). The scheme of the invention is suitable for crowd screening, disease early warning and individualized intervention decision.

The application relates to the technical field of disease prognosis risk early warning, and particularly discloses a disease prognosis risk early warning method and system based on big data, which comprises the following steps: obtaining all reference prediction patients of a current patient based on all types of health data of the current patient; obtaining the evaluation and assignment of each type of health data of the current patient based on each type of health data of the current patient and an evaluation and assignment model of each type of health data; obtaining a disease early warning reference graph and a disease early warning comparison graph of the current patient based on the evaluation and assignment of all types of health data of all reference prediction patients of the current patient; and obtaining a disease prognosis risk estimation value of the current patient based on the disease early warning reference graph and the disease early warning comparison graph of the current patient, and then obtaining a disease prognosis risk early warning result of the current patient. The application realizes accurate quantification of the degree of future development and recovery risk of the disease of the current patient, improves the accuracy and reliability of disease early warning, and ensures effective use of resources.

The invention discloses a data processing device and system for detecting the aging degree and application of the data processing device and system. The technical problem to be solved is to detect the aging degree of an organism to be tested or cells to be tested. Specifically disclosed is a device, the device comprising: M1, a migration body condition acquisition module for acquiring the migration body condition of a subject body or a subject cell, the migration body condition comprising the number of migration bodies or the content of the migration bodies; the result output module is used for comparing the condition of the migration body of the tested organism or the tested cell with a reference value and giving a result, and the result comprises at least one of the following: C1, the aging degree of the organism or the cell; c2, physiological age of the organism or the cell, C3, whether the organism or the cell suffers from the senescence-related disease or not or the risk of the senescence-related disease, and C4, the senescence and / or age-related disease early-warning result can be judged through the device, and the senescence degree of the tested organism or the tested cell can be judged through the device.

Early stage, rapid, low-cost detection of disease components in a biological sample is critically important. A point of care device can include a collection region and can be used to hold a sample that is combined with a fluorescent dye and a plurality of magnetic particles such that disease components in the sample are tagged with the fluorescent dye and the plurality of magnetic particles. At least one magnet can be located next to the collection region to establish a magnetic field gradient to draw the tagged disease components into the collection region from the device. A fluorescence microscope can image the small collection region based on the fluorescent dye to detect the disease components. The fluorescence microscope uses light to excite the fluorescent dye and a filter to transmit light emitted by the fluorescent dye to the fluorescence microscope, while restricting light used to excite the fluorescent dye.

The invention discloses a biomarker for predicting prognosis of BPA-induced intrahepatic cholangiocarcinoma, a scoring model and application, and belongs to the technical field of biomedicine. The biomarker is composed of eight genes, namely, GAPDH (Glyceraldehyde-3-phosphate Dehydrogenase), HSP90AA1 (Hepatocyte Specific Protein 90AA1), CTNNB1 (Cytotoxic Nuclear Nuclear Nuclear Nuclear B1), NFKBIA (Nuclear Factor Kinase Kinase Institute), HSPA5 (Hepatocyte A risk scoring model is constructed by utilizing the biomarkers, and by calculating risk scores, clinical recognition of intrahepatic cholangiocarcinoma prognosis high-risk patients can be assisted in advance, so that an effective strategy can be adopted in an early stage of a disease, the recurrence probability is reduced, the lifetime of the patients is prolonged, and an accurate basis is provided for formulating an individualized treatment scheme.

Methods and systems for the detection and diagnosis of diseases, including cancer. [Solution] A patient's sample is analyzed by absorption spectroscopy in the near-infrared and mid-infrared ranges to create a spectral signature, and the sample is obtained from the patient. This signature is processed by a computational engine using one or more machine learning techniques to determine whether the spectral signature indicates the presence of a disease, including one or more cancers. The embodiment operates outside the conventionally accepted wavelength range, facilitating faster, simpler, and more reliable testing.

The invention relates to an infectious disease morbidity prediction method and system based on three-layer dimension parallel convolution, and belongs to the technical field of artificial intelligence and public health monitoring. Aiming at the problems of difficulty in collaborative modeling of multi-source data, difficulty in balancing long-term trend and short-term fluctuation and lack of adaptive feature fusion in an existing prediction method, the method comprises the following steps: firstly, preprocessing input time sequence data and decomposing the data into coarse, medium and fine granularity features; then long-term trend, medium-term period and short-term fluctuation characteristics are extracted through three parallel branches of a kernel adaptive network, a multi-scale time sequence convolution network and a time sequence convolution module respectively; and then the multi-scale features are subjected to adaptive weighted fusion by using a gating fusion unit, and finally a prediction value is output through a full connection layer. The method is mainly used for accurately predicting the morbidity of infectious diseases and provides support for disease early warning and prevention and control resource allocation.

A diagnostic platform that enables multi-disease diagnostic panels which will help primary care physicians track the health status of patients as well as recognize disease early. The diagnostic platform implements a method of biomarker selection and tiered Artificial Intelligence (A.I.) approach comprising a multi-level machine / deep learning (ML / DL) system which is using multi-panels of biomarkers.

The invention discloses a method for auxiliary assessment of early risk of Alzheimer's disease based on EFEMP1 detection. The method is an in-vitro non-diagnostic analysis method, and comprises the following steps: detecting the biomarker level of EFEMP1 in a biological sample of a subject, and obtaining information for evaluating the AD early risk of the subject based on a detection result. The biomarker comprises an EFEMP1 genotype, an EFEMP1 mRNA (messenger Ribonucleic Acid) expression level or an EFEMP1 protein concentration. When the risk assessment is realized by quantitatively detecting the concentration of the EFEMP1 protein in the plasma, an immunodetection kit containing a capture antibody and a detection antibody aiming at different epitopes of the EFEMP1 protein can be specifically adopted. Objective information for risk judgment is obtained by detecting the protein concentration, genotype or mRNA level of EFEMP1 in a biological sample of a subject, early-stage and non-invasive monitoring of an AD upstream pathological mechanism is achieved, and a new strategy is provided for early warning of diseases.

The invention discloses an automatic identification method and system for an infectious disease risk place. The method comprises the following steps: acquiring symptoms and spatio-temporal trajectory information of each case; generating a symptom semantic feature vector of each case according to the symptom, and respectively generating a space mapping vector and a time mapping vector of each case according to the spatio-temporal trajectory information; carrying out feature fusion on the vectors, constructing a spatio-temporal index of each case, and obtaining a spatio-temporal index data set; carrying out clustering analysis by adopting an adaptive density clustering algorithm, and carrying out feature mapping processing on a clustering result to obtain a feature mapping vector of each case; and performing visual display on the feature mapping vector by using a visualization engine so as to automatically identify the infectious disease risk site. According to the method, the infectious disease risk site can be quickly, accurately and visually identified, a foundation is laid for deeply carrying out disease early warning, and a theoretical basis is provided for formulating prevention and treatment strategies and measures.

The invention discloses application of integrin alpha3 as a focal segmental glomerulosclerosis biomarker, and belongs to the technical field of biological detection. A sample used for detection is kidney tissue, and the expression level and distribution of integrin alpha3 in the sample are detected. By detecting and analyzing the expression level and distribution of the integrin alpha 3 in the epithelial cells of the cyst wall layer in the glomerulus, the segmental sclerosis globules in the early stage of the disease can be identified, and the method is an effective method for assisting in diagnosing focal segmental glomerulosclerosis.

The invention relates to an early disease detection device based on high-depth cell-free nucleic acid sequencing, and the device can comprise a library preparation part which is used for preparing a library set comprising a whole genome sequencing library, a whole epigenome sequencing library, a transcriptome sequencing library and an epitranscriptome sequencing library based on cell-free nucleic acid extracted from blood; the sequencing part is used for sequencing the library set to generate high-depth cell-free nucleic acid sequencing data; the signal calculation part is used for mapping the high-depth cell-free nucleic acid sequencing data to a genome reference sequence so as to determine the position of the high-depth cell-free nucleic acid sequencing data on a genome, and calculating at least one disease-specific signal value based on the mapped high-depth cell-free nucleic acid sequencing data; a prediction signal value generation section for processing the at least one disease-specific signal value to generate a disease prediction signal value; and a prediction model generation unit for generating a disease prediction model using the marker information relating to the blood and the disease prediction signal value to detect the occurrence or absence of a disease in the input blood and the type of the disease.

The invention discloses an auxiliary decision-making system for early diagnosis and progress prediction of cerebral small vascular diseases. The system comprises a data acquisition module, a data processing module, a comprehensive analysis module and a data storage module, the data acquisition module is used for acquiring proteomics detection data of a patient; the data processing module is used for analyzing the detection data of the patient to obtain analysis results of different detection data; the comprehensive analysis module is used for carrying out comprehensive analysis on analysis results of different detection data and generating auxiliary decision prompt information; and the data storage module is used for storing the data of the patient. Through comprehensive analysis of iron metabolism imbalance detection data and protein detection data of a patient, the disease early diagnosis sensitivity and disease early prediction accuracy are improved, and meanwhile, the accuracy of a diagnosis report auditing result is improved.