25 results about "Disease rates" patented technology

The invention discloses a lung disease diagnosis and grading method based on quantitative CT image omics and deep learning. According to the method, through multi-dimensional information integration and model optimization, two core tasks of disease judgment and disease condition grading can be completed at the same time, and comprehensive support is provided for clinical diagnosis and treatment. The model sets feature priorities by referring to clinical diagnosis logic in the training process, the judgment result and the grading standard are completely matched with clinical general specifications, and the model can be directly applied to diagnosis and treatment decision-making without secondary conversion of doctors. After multi-center clinical verification and iterative optimization, the stability and the accuracy of the model are fully guaranteed, subjective errors caused by manual film reading can be effectively reduced, the diagnosis efficiency can be improved, the chronic obstructive pulmonary disease screening capability of primary medical institutions can be remarkably improved, early diagnosis and early treatment of more patients can be helped, and the clinical application prospect is wide. Therefore, the morbidity and disability rate of diseases are reduced, a clinical management path is optimized, and the overall disease burden is relieved.

The invention discloses a Chikungunya thermal seasonal propagation risk assessment method and system based on a coupling model. The method comprises the following steps: acquiring basic data and preprocessing the basic data; model parameters are divided into a fixed type and a dynamic type, the dynamic parameters are combined with environmental data to calculate and assign values, historical epidemic situation data in the past three years are used as target values, key parameters are adjusted through a least square method, and the error between a model simulation epidemic situation curve and an actual observation value is minimized. Based on the influence of a standardized meteorological data quantification environment on mosquitoes, generating a day-by-day key parameter sequence and inputting the day-by-day key parameter sequence into a medium module; the age structure model is used for simulating population dynamics of mosquitoes at all stages, and the SEI model is used for simulating transmission of viruses in the mosquitoes. The number of infectious mosquitoes output by the medium module is used as a drive, crowd state conversion is simulated through an SEIR model, and the number of infected people every day and the morbidity are obtained; and risk level assessment of the assessment area is completed, and quantitative data support is provided for risk assessment.

The application relates to a chronic kidney disease prediction method based on clinical information graph representation, belongs to the technical field of computer-aided diagnosis of chronic kidney disease (CKD), and aims to solve the problem of missed diagnosis caused by the fact that the kidney function index of early CKD is not obvious. The incidence of CKD is high, the early symptoms are hidden, and the disease is easy to be missed, thus developing into end-stage renal disease. Therefore, the application provides a chronic kidney disease prediction method based on clinical information graph representation. The method first extracts fundus image and clinical index features; the clinical index is fused into a clinical index joint feature through text embedding and numerical feature fusion, and the joint feature is fused with the fundus image feature through cross-modal attention; the similarity between subjects is calculated based on the fused feature, and a subject relationship graph is constructed by using an adaptive dynamic threshold mechanism; finally, a hybrid graph neural network is used for graph representation learning, pathological similarity between subjects is mined, and accurate prediction of chronic kidney disease is realized. The application can improve the detection rate of early CKD and is applied to non-invasive early screening and risk early warning.

Systems and methods for ascertaining cancer-related risks resulting from multi-cancer early detection (MCED) tests administered to individuals using population-level classification data are disclosed. According to certain aspects, an electronic device may access a data structure reflecting a prevalence dataset, an incidence dataset, a dataset on instances of interval cancers, or some combination thereof. The electronic device may analyze a result of an MCED test administered to an individual in combination with the data structure to determine a set of risk levels of the individual developing one or more of the plurality of cancers over a specified time period, and generate a digital report for the individual which provides a comprehensive post-test analysis.

Chronological age is an important predictor of morbidity and mortality, however it is unable to account for heterogeneity in the decline of physiological function and health with advancing age. Several attempts have been made to instead define a “biological age” using multiple physiological parameters in order to account for variation in the trajectory of human aging; however, these methods require technical expertise and are likely too time-intensive and costly to be implemented into clinical practice. Accordingly, a metabolomic signature of biological aging was developed that can predict changes in physiological function with the convenience of a blood sample. A weighted model of biological age was generated based on multiple clinical and physiological measures in a large group of healthy adults and was then applied to a cohort of healthy older adults who were tracked longitudinally over a 5-10 year timeframe. Plasma metabolomic signatures were identified that were associated with biological age, including some that could predict whether individuals would age at a faster or slower rate. These results not only have clinical implications by providing a simple blood-based assay of biological aging, but also provide insight into the molecular mechanisms underlying human healthspan.

The invention provides a chronic disease health-care system for institutional old-age care, and relates to the field of intelligent medical treatment. The system comprises a periodic physical examination module for performing periodic physical examination on a crowd to obtain a physical examination result; the grading and bidding module is used for dividing the crowd into low-risk, medium-risk and high-risk groups according to the physical examination result; the low-risk group, the medium-risk group and the high-risk group are managed by units of different levels; the high-risk group management module at least comprises a chronic disease special case unit and is mainly used for creating a chronic disease file for a patient and performing chronic disease management service on the patient; the chronic disease type comprises any one or more of hypertension, diabetes, cerebral apoplexy, coronary heart disease, chronic obstructive pulmonary disease and fundus disease. According to the invention, a patient-centered'Internet + chronic disease management 'mode is adopted, chronic diseases are classified, graded and bid-divided, a county hospital-community / town health center-health station integrated system network is formed, early discovery of chronic diseases is truly realized, the morbidity of high-risk groups is reduced, and the disability and fatality rates are reduced.

The present invention discloses an ecological planting method for reducing the disease infection rate of Pseudostellaria heterophylla and improving its yield and quality by covering with shading screens, belonging to the technical field of medicinal plant cultivation. A shading facility is built in the middle and late growth stages of Pseudostellaria heterophylla under high temperature, strong light and rainy conditions; the method for building the shading facility is as follows: in the middle growth stage of Pseudostellaria heterophylla, a support is built above the planting area, and a shading screen is covered on the support to adjust the light intensity in the field. The planting method of Pseudostellaria heterophylla is applicable to the planting area of Pseudostellaria heterophylla in the south with high temperature and high humidity. The middle stage is the middle of April, and it is covered until the harvest of Pseudostellaria heterophylla in early July. By building a shading facility, the microclimate environment for the growth of Pseudostellaria heterophylla is optimized, thereby reducing the incidence of plant diseases, promoting photosynthetic growth, improving yield and quality, etc. This method has the advantages of simple operation, low cost, ecological environmental protection, etc., can significantly improve the yield and quality of Pseudostellaria heterophylla, and achieve the synergistic effect of disease prevention and control and growth regulation.

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.

The invention discloses an infectious disease prediction method, system and device based on multi-source big data and a medium, and relates to the technical field of infectious disease prediction. The method comprises the following steps: acquiring multi-dimensional data; the multi-dimensional data comprises legal infectious diseases, atmospheric pollutants, meteorological factors and social environment elements; feature weight sorting is carried out on the multi-dimensional data to obtain recombination features which influence the importance sorting of the infectious disease morbidity; and constructing an infectious disease prediction model, carrying out model training by using the recombinant features and the corresponding infectious disease morbidity labels, and using the trained infectious disease prediction model for infectious disease morbidity prediction at the next time point to obtain a final prediction result. According to the invention, high-precision fitting prediction of the incidence of infectious diseases can be realized.

The invention relates to the technical field of risk assessment, in particular to an insurance risk dynamic assessment method and system using big data analysis, and the method comprises the following steps: obtaining the migration path and time information of insured persons, constructing a behavior sequence, extracting target indexes, recognizing a high-risk region, matching overlapped nodes, and integrating the characteristics of path segments. According to the method, by constructing the individual migration trajectory and combining the regional epidemic situation indexes, dynamic identification and regional risk description of cross-domain behaviors and association of the migration frequency, the morbidity, the number of sickbeds and other factors are realized, the risk identification pertinence is enhanced, the people flow density and the service capability of path intersection are analyzed, and the risk level of the insurance risk is improved. Identifying propagation key nodes, combining migration density and infection intensity, generating a space-time risk index grid, mapping propagation potential, extracting risk levels based on an individual path and grid risk overlapping region, realizing insurance risk dynamic marking, and improving evaluation accuracy and differentiation level.

The invention belongs to the technical field of medical information, and relates to a multi-scale tuberculosis morbidity prediction method and system.The system collects data through a data collecting and processing module to generate a spatio-temporal feature data set, and high-quality input is provided for subsequent model training; a space-time association graph structure between regions is constructed through a space-time association network construction module, and structured input is provided for a GCN-LSTM model; the GCN-LSTM model training module uses a graph convolutional neural network and a long short-term memory network to train multi-source spatio-temporal data, and is used for capturing spatial dependence between regions and a dynamic evolution trend of a time sequence; and the space-time prediction and result output module uses the trained GCN-LSTM model and the latest multi-source data to predict the tuberculosis morbidity of each region under different time scales in the future. According to the invention, the precision and real-time performance of tuberculosis morbidity prediction can be effectively improved, and technical support is provided for accurate prevention and control.

The invention provides a multisystem disease analysis method based on gastrin plasma proteomics, which comprises the following steps of: A, analyzing a large number of adult individuals subjected to baseline evaluation by using queue research data of a biological sample library, and measuring a plurality of plasma proteins; b, analyzing a standardized protein expression (NPX) value of gastrin (GAST) by adopting a multivariable Cox proportional risk model, and correcting by using Bonferroni so as to reduce a false positive result; and C, exploring the correlation between the GAST-PRS and the disease phenotype by adopting full phenotype correlation analysis (PheWAS) based on polygene risk score (PRS), and further analyzing the genetic correlation between the GAST and the disease by using linkage imbalance score regression (LDSC). According to the method, the GAST can be redefined as a cross-system risk regulation factor, and the method is of great significance to layering of patients with shared pathway imbalance. The GAST has multi-system correlation with morbidity and mortality in the outcome of 168 diseases, and challenges the classical normal form of the gastrointestinal specific function of the GAST. These findings establish the location of GAST as a multi-system risk indicator.

An object of the present invention is to make it possible to easily determine a presence or absence of ketosis and / or the possibility of ketosis in an animal on site or the like. Another object of the present invention is to provide an apparatus for performing the determination. A method for determining ketosis according to one embodiment of the present invention includes providing gas detection means with a gas generated from a body fluid collected from an animal (except for humans), and determining ketosis of the animal based on a composition of the gas generated from the body fluid using a response of the gas detection means to the gas generated from the body fluid.

The present invention relates to a biomarker composition for diagnosing asthma caused by SARS-CoV-2 infection, and uses thereof. Through a change in the expression of CDHR3, SCGB3A2, PLAU, or NPY, which are biomarkers according to an embodiment of the present invention, it was confirmed that patients infected with SARS-CoV-2 have a higher incidence of asthma compared with healthy individuals, and thus the biomarker can be widely applied in the fields of diagnosis of SARS-CoV-2-induced asthma and drug screening.

The present application relates to the technical field of plant pathology and agricultural science research, more particularly to a rice seedling blight inducing device and method, wherein the device comprises a box body, a box cover, a first measuring cup and a second measuring cup, the box cover is detachably connected to the top of the box body, the box body is a transparent structure, the box cover is provided with air holes, the bottom of the box body is provided with a drainage hole, the side wall of the box body is provided with a scale, the capacity of the first measuring cup is greater than that of the second measuring cup, and the first measuring cup can be inverted to cover the second measuring cup to form a closed germination cavity. The present application has the advantages of simple structure and facilitating operation standardization; through the sandwich type matrix structure of "bottom moisture layer + seed layer + top covering layer", combined with the two-stage culture mode of indoor constant temperature disease induction and outdoor natural adversity induction, the disease incidence is significantly improved, and disease identification is facilitated.

This application discloses an immune-inducing regulator with dual effects of increasing yield and resisting disease, and its application in chestnuts. The immune-inducing regulator uses *Sphingomonas sphingosine monocytogenes* as its active ingredient, working by coordinating a "growth-defense" trade-off strategy. It downregulates defense response genes related to plant-pathogen interactions and endoplasmic reticulum protein processing in the MEbrown module to reduce defense energy consumption, while upregulating genes related to sugar metabolism and phenylpropane biosynthesis in the MEyellow module to promote substance accumulation and cell wall strengthening. Application results showed that, compared with the control group, the transverse diameter, longitudinal diameter, and single-grain weight of chestnuts in the treatment group were significantly increased (P<0.01), the incidence of fruit rot during storage was significantly reduced, and the control efficacy remained stable at over 70% within 3 months. This invention achieves dual optimization of "increased yield" and "disease resistance," providing not only a new green control approach based on the core microbiome for the green and efficient production of chestnuts, but also an ideal source of inoculum for plant immune regulators.

The invention belongs to the technical field of microorganisms, and particularly relates to tarlaromyces flavus and application thereof. The invention discloses tarlaromyces flavus H12, and the preservation number of the tarlaromyces flavus H12 is CGMCC No.42336. The tarlaromyces flavus H12 is separated and screened from the nature for the first time, and can generate a remarkable antagonistic effect on a pathogenic bacterium-fusarium oxysporum strain causing the root rot of the fritillaria taipaiensis. Through identification, the fermentation liquor and volatile substances of the tarlaromyces flavus H12 can destroy the structure of pathogenic bacteria filaments to cause malformation and distortion of hyphae, and the inhibition rates of the tarlaromyces flavus H12 on the growth of the hyphae respectively reach 58.54% and 28.02%. Potting and field experiment results show that the morbidity and the disease index of the Fritillaria taipaiensis root rot can be remarkably reduced by applying the H12 fermentation liquor, and the final control effect reaches 56.16% and can be equivalent to that of chemical pesticides.

The application belongs to the technical field of disease animal model construction method, and particularly relates to a construction of an animal model of rheumatoid arthritis. The application uses SIRT3 knockout female mice, induces an arthritis model through mBSA antigen, and after primary immunization and secondary immunization, the rheumatoid arthritis phenotype appears one week after the secondary immunization. The mouse obtained in the application can appear knee joint injury, local joint inflammatory cell infiltration and obvious increase of inflammatory factors after one to two weeks of modeling. The rheumatoid arthritis mouse model obtained in the application has short modeling cycle, high and stable incidence rate, and clear pathogenesis, and is convenient for researchers to widely use.

This application discloses a device and storage medium for calculating incidence rates based on medical record data. The device includes: a data acquisition module for acquiring medical record data from multiple patients; a data filtering module for filtering the medical record data from multiple patients to obtain medical record data from a target patient suffering from a rare disease; a data prediction module for analyzing the number of second inpatient cases provided by a second category of medical institutions based on the number of first outpatient cases and first inpatient cases provided by a first category of medical institutions to obtain a prediction result of the number of second outpatient cases suffering from rare diseases in the second category of medical institutions; and a first data determination module for determining the incidence rate of the rare disease based on the medical record data of the target patient and the prediction result. Thus, even in the case of missing medical data, a relatively reliable incidence rate estimation scheme can still be achieved, thereby improving the accuracy of the final obtained incidence rate of rare diseases.

The application provides a breast cancer absolute risk prediction method based on generative representation and relative risk decomposition, which receives a mammography image, clinical factors, region identification and age, firstly determines an annual baseline incidence rate sequence according to the age and the region; uses a generative representation model to perform imaging style generation and continuous constraint on the image, aligns the breast structure, and extracts a feature vector reflecting long-term tissue state; then inputs the feature vector and the clinical factors into a staging risk calculation framework to obtain a relative risk coefficient, and uses an imaging style variant set to perform consistency test and parameter correction; on this basis, the absolute risk sequence is recursively synthesized according to the baseline incidence rate by year, the risk is determined according to a threshold, and a screening interval is obtained; the application can output individual absolute risk and screening interval on a unified annual time axis, weaken the influence of imaging style difference on the evaluation result, and improve the stability of risk prediction.

The invention discloses a PEDV (porcine epidemic diarrhea virus) subunit vaccine, a preparation method and application, and belongs to the technical field of preparation of PEDV vaccines.The PEDV subunit vaccine is prepared by mixing and emulsifying PEDVS recombinant protein and MONTANIDEISA201VG adjuvant, the vaccine can effectively prevent PEDV viruses and reduce the morbidity, and the challenge protection rate is 100%.

Devices, systems and methods for non-invasive modulation of the baroreflex system of a patient. In embodiments, the present disclosure may be used to measure and monitor baroreflex function for diagnostic purposes in patients acutely or chronically to inform and guide medical treatment, assess disease severity, or assess morbidity / mortality risk. In embodiments, the present disclosure may be used to provide non-invasive baroreflex activation therapy acutely or chronically to treat a variety of disease conditions through rebalancing of the sympathetic and parasympathetic limbs of the autonomic nervous system and their connections to higher brain centers.

The invention discloses a grading early warning method for influenza epidemic intensity, and belongs to the field of infectious disease monitoring and early warning. The method comprises the following steps: collecting related historical monitoring data of influenza, calculating corresponding indexes, taking the influenza report morbidity as a target sequence, drawing a time sequence comparison diagram of the influenza report morbidity and other indexes according to the indexes, and screening indexes capable of being used for monitoring and early warning by performing time sequence diagram comparison with other indexes and constructing lagging correlation analysis; according to the screened indexes, adopting a comprehensive evaluation method to calculate an influenza comprehensive index; and constructing a grading early-warning model of a mobile epidemic interval method based on the influenza comprehensive index, and calculating a grading early-warning threshold value of the influenza epidemic intensity. According to the method, the limitation of a single data source and the influence of a subjective judgment standard on an early warning result are effectively avoided, the influenza epidemic situation can be reflected more objectively and comprehensively, and a reliable basis is provided for formulating a scientific prevention and control strategy.

This method for predicting the incidence of disease caused by parasites comprises the steps in which: a prediction device receives information about a subject; the prediction device inputs the information about the subject to a prediction model; and the prediction device predicts the incidence of disease caused by parasites of the subject on the basis of an output value of the prediction model. The information about the subject may include at least one of sex information, age information, body mass index (BMI) information, or alcohol.