56 results about "Disease rates" patented technology

The invention discloses a diabetic nephropathy risk assessment and prediction system based on platelet indexes, and particularly relates to the technical field of disease risk prediction, which comprises a platelet index acquisition module, a drug use data acquisition module, a drug intervention assessment module, an index correction module, a clustering analysis module and a diabetic nephropathy risk assessment module, the method comprises the following steps: acquiring platelet related detection indexes and historical medication data of a patient, calculating a drug intervention influence index and generating a correction factor, realizing platelet index reverse reduction correction by adopting a convolutional neural network model, determining a similar group in combination with clustering analysis, and dynamically evaluating the risk of diabetic nephropathy based on the average morbidity of the group. Personalized accurate prediction is realized; the platelet index correction accuracy is improved, individualized drug intervention elimination is achieved, the similar population clustering analysis precision is enhanced, diabetic nephropathy risk prediction is dynamically adjusted in combination with comprehensive scores, and the early screening and individualized intervention effects are improved.

Endometrial cancer (EC) is the most common gynecologic cancer in developed countries and the fourth most common cancer affecting women in the US. In contrast to other cancers, rates of EC continue to rise, and there are indications that social determinants of health and race and / or ethnicity contribute to risk. Thus, the methods described herein provide a non-invasive means of measuring biomarkers in the local cervicovaginal microenvironment. These novel biomarkers may be used for diagnosing and / or predicting women who are "at risk" for the development and progression of endometrial cancer (EC; e.g., EC type 1). Specifically, the methods herein may include obtaining a cervicovaginal lavage (CVL) or vaginal swab sample from a patient and producing a profile of at least five or more biomarkers from the collected sample.

The invention relates to the technical field of microbial agents, and discloses a composite microbial agent, a preparation method thereof and application of the composite microbial agent in prevention and treatment of potato scab. Wherein the microbial complex microbial inoculant is prepared from bacillus aglaia pairwise 6096, streptomyces sp. 6219 and bacillus atrophaeus 4618; the aglaia odorata pairwise bacillus 6096, the streptomyces 6219 and the bacillus atrophaeus 4618 are all preserved in the China General Microbiological Culture Collection Center (CGMCC), and the preservation numbers of the aglaia odorata pairwise bacillus 6096, the streptomyces 6219 and the bacillus atrophaeus 4618 are respectively CGMCC NO.34691, CGMCC NO.34692 and CGMCC The composite microbial inoculant is good and stable in antibacterial effect, and the morbidity and disease index of potato scab can be remarkably reduced after the composite microbial inoculant is used.

The invention provides a disease spatial distribution analysis method and device based on environmental risk factors, and the method comprises the steps: building a method framework integrating case data, geographic data and environmental data, and evaluating the influence of the environmental risk factors on the disease morbidity spatial distribution on a fine spatial scale; the influence of the spatial-temporal heterogeneity of the environmental risk factors on the spatial distribution of the diseases is considered in detail, and the relationship between the environmental risk factors and the spatial distribution of the diseases is predicted by using a geographically weighted regression model, so that a new thought and a new method are provided for understanding the causes and influence factors of the spatial distribution of the diseases; and high-quality data support is provided for formulating targeted environment intervention measures and disease prevention and control strategies.

Deep learning based systems and methods for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs) are disclosed. The methods are clinically validated by external population cohorts wherein to apply a machine-learning classifier having been trained using a dataset of CFPs of a longitudinal patient cohort regarding glaucoma development of each of the patients in the cohort over a period of time (e.g., over the course of a few vears), to predict a likelihood of glaucoma incidence or progression for the patient in the future (e.g., over a similar period of time of several years).

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 provides a marker for predicting caries occurrence based on saliva metabolites, which is characterized by comprising 4-acetaminobutyric acid, adipic acid, pantetheine and palmitoyl ethanolamide, and the marker is used for preparing a kit for detecting the caries occurrence risk of a subject. The invention provides four markers in saliva metabolites, and early warning of future caries can be realized in a non-invasive and low-cost manner; according to the basis of detection numerical values of the markers, the morbidity state of the constant dentition caries can be reflected, so that accurate prevention and control of the caries can be realized, and finally the purposes of reducing the morbidity of the caries, helping disease pathological typing and researching drug action targets, accurate medication and pathogenesis can be achieved.

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.

This invention discloses a heatstroke prevention and monitoring system based on conscious cognitive behavioral characteristics, belonging to the field of intelligent medical technology. This invention addresses the problems of existing technologies lacking intelligent analysis and exhibiting a certain degree of subjectivity, making it difficult to ensure the effectiveness of heatstroke prevention and monitoring. It analyzes the patient's physiological signals and behavioral data using a linear discriminant analysis model to predict whether the patient has heatstroke. After predicting heatstroke, a weighted average method is used to further calculate the patient's risk index, and different prevention and monitoring strategies are implemented based on the risk index. Based on this, it can effectively identify whether a patient has heatstroke and the risk index, thereby enabling targeted preventative measures to avoid further disease progression. Simultaneously, through intelligent analysis and judgment, it can effectively reduce the incidence of heatstroke, reduce the consumption of medical resources, and lower the socioeconomic burden.

The invention discloses a model suitable for predicting metabolic syndrome (MetS) of people with polycystic ovarian syndrome (PCOS), a construction method and application, the model is characterized in that values are respectively assigned on the basis of indexes of a plurality of predictive variables, the predictive variables of the model are calculated, and the predictive variables of the predictive variables of the model are calculated according to the predictive variables of the predictive variables of the model, so that the predictive variables of the predictive variables of the model can be calculated according to the predictive variables of the model. The predictive variable is selected from sex hormone binding globulin, acne, maternal family menoxenia incidence history, age, apolipoprotein-B / apolipoprotein-A, fasting insulin and body mass index; and fitting the PCOS with MetS prediction model by adopting Logistic regression, and obtaining the morbidity according to the prediction model. According to the scheme, by optimizing model design, the requirement for the number of indexes is lowered, so that the application difficulty is lowered while the prediction precision is improved, and the prediction efficiency is remarkably improved.

The invention discloses an efficient feeding method for meat rabbits. The method comprises the following steps that biological characteristics are dynamically monitored, wherein individual weight, body surface temperature, heart rate and motion trail data are obtained in real time through an intelligent necklace; dynamic genetic breeding: screening high-quality breeding rabbits based on the monitoring data; intelligent grouping regulation and control: dynamically adjusting a group structure according to individual behavior characteristics, wherein the grouping period is 72 + / -12 hours; accurate nutrition supply: optimizing a feed formula according to real-time growth data, and improving the digestibility by adopting a steam conditioning technology; intelligent environment regulation and control: a temperature and humidity sensor is linked with a water curtain-fan system; disease early warning prevention and control: integrating biological characteristics and environmental data, and early warning disease outbreak 72 hours in advance. Therefore, by realizing data-driven management of the meat rabbit breeding process, the feed conversion rate and the growth efficiency are effectively improved, the morbidity is reduced, and the comprehensive breeding benefit is improved.

The invention relates to the field of intelligent medical treatment, in particular to an AI-based medical decision weight distribution method, which comprises the steps of determining a data extraction mode according to data richness; during multi-data source extension selection, determining a data matching degree according to a historical morbidity and a rhythm disorder coefficient, and taking data with the data matching degree greater than a preset data matching degree as to-be-supplemented data; determining a weight coefficient of a related parameter of the data matching degree according to the critical threshold value; according to the effective data relevancy, judging that the supplement selection mode is to select the medical record data according to the feature keywords or select the medical record data according to the dynamic difference coefficient; determining a weight adjustment amplitude according to a reference weight distribution mode or a diagnosis and treatment validity difference value according to a weight adjustment mode corresponding to the effective data relevancy determined according to the diagnosis and treatment stability coefficient; when the weight adjustment amplitude value reaches a preset adjustment amplitude value, weight adjustment is ended; and the accuracy and efficiency of medical record file matching are improved.

The invention discloses an upper respiratory tract infection disease early warning system and method based on environmental factors, and belongs to the field of infection disease early warning. Infection cardinal number anomaly analysis is carried out based on the change condition of the number of cases, and infection anomaly analysis is carried out based on an infection expansion anomaly analysis result and an infection cardinal number anomaly analysis result. According to the method, early warning and effective prevention and control of diseases can be realized by establishing an upper respiratory tract infection disease early warning method integrating environmental factors, infection intensive factors and infection cardinal factors, and the system can improve the cognition degree and self-protection awareness of the public for the diseases, so that the system is suitable for popularization and application. Disease morbidity and mortality are reduced, so that public health safety is guaranteed.

The invention discloses a kit for screening cerebral arterial thrombosis risk and a screening method, and belongs to the technical field of biological detection. The kit for screening the cerebral arterial thrombosis risk is a kit for detecting gene mutation of a C677T site of an MTHFR gene and comprises a forward primer and a reverse primer, the sequence of the forward primer is shown as SEQ ID NO.1, and the sequence of the reverse primer is shown as SEQ ID NO.2. The kit for screening the cerebral arterial thrombosis risk is a kit for detecting gene mutation of the C677T site of the MTHFR gene. The invention creatively finds that the gene polymorphism of the C677T site of the MTHFR gene has certain relevance with the disease of the cerebral arterial thrombosis, the site mutation is one of high-risk factors of the cerebral arterial thrombosis, and the A1298C site mutation of the MTHFR gene is not obviously associated with the susceptibility of the cerebral arterial thrombosis. The risk of the cerebral arterial thrombosis is evaluated by screening a susceptibility gene locus, the operation is simple, the cost is reduced, the prevention of the cerebral arterial thrombosis is enhanced, and the incidence rate of the cerebral arterial thrombosis is further reduced.

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 weak subtype recognition method and device based on machine learning and metabonomics characteristics. The method comprises the steps of data acquisition and preprocessing, wherein 251 metabolites are analyzed and subjected to standard normalization processing; dividing the data into a training set and a test set; a weak subtype identification step: using a CatBoost algorithm to analyze metabolism characteristics, selecting the first 11 key metabolites with the highest SHAP value sequence, and layering weak participants into four subtypes through principal component analysis and K-means clustering analysis; and a weak subtype, chronic disease and all-cause mortality rate association evaluation step: displaying the accumulated morbidity difference of four subtypes and non-weak participants on 13 chronic diseases and all-cause mortality rates by using a Kaplan-Meier curve, and checking the association between the novel weak subtypes and the results by using a multivariable Cox proportional risk model. Based on machine learning and metabonomics characteristics, novel weak subtypes are identified, the application value of the novel weak subtypes in chronic disease management and prognosis is evaluated, and a new basis is provided for clinical intervention and health management.

The present invention discloses a rice bacterial blight monitoring method and system based on multi-source data analysis. The proposed rice bacterial blight monitoring method analyzes multi-source data using a principal factor analysis method to analyze the weight of each influencing factor data on the incidence rate or disease index, thereby screening out the main influencing factors that cause the onset of rice bacterial blight. A monitoring strategy is generated based on the size of the influence weight value. By setting up monitoring units, a real-time monitoring strategy is implemented for the main influencing factors, and an intermittent monitoring strategy is implemented for minor or irrelevant influencing factors. This allows for purposeful and efficient monitoring of the spread trend of rice bacterial blight, facilitating timely implementation of corresponding prevention and control measures for the main influencing factors that cause rice bacterial blight, thereby effectively curbing the rapid spread of rice bacterial blight within rice fields.

The invention discloses a heat stroke prevention and monitoring system based on consciousness cognitive behavior feature recognition, and belongs to the technical field of intelligent medical treatment. The problems that in the prior art, intelligent analysis is lacked, certain subjectivity exists, and the effectiveness of preventing and monitoring the heat stroke is difficult to ensure are solved, the physiological signals and behavior data of the patient are analyzed through the linear discriminant analysis model, and whether the patient suffers from the heat stroke or not is predicted; after it is predicted that the patient suffers from the heat stroke, a weighted average method is adopted to further calculate the risk index of the patient, and then different prevention and monitoring strategies are applied to the current patient according to the risk index; on the basis, whether the patient suffers from the heat stroke and the risk index of the patient can be effectively identified, so that targeted prevention measures can be taken, and further development of the disease is avoided; meanwhile, through intelligent analysis and judgment, the morbidity of heat stroke can be effectively reduced, consumption of medical resources is reduced, and social and economic burdens are reduced.

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.

Cerebral apoplexy has the characteristics of high morbidity, high mortality, high disability rate and the like, and is taken as three major diseases threatening human health together with cancer and coronary heart disease. The current research trend focuses on the hybrid architecture design of CNN and Transform, and aims to improve the cerebral apoplexy segmentation performance through the complementary advantages of the CNN and Transform. However, an existing method generally has a key defect, pays attention to simple combination of network structures, neglects the inherent inconsistency problem of two architectures in a feature learning process, and restricts further improvement of model performance. Therefore, the invention provides a cerebral apoplexy focus segmentation method based on a hybrid encoder and edge enhancement. The invention provides an edge enhancement module aiming at the problems of irregular lesion shape, fuzzy boundary and low contrast ratio. In order to solve the problem of inconsistency of feature learning between CNN and Transform double encoders, a hybrid encoding module is provided, and progressive feature fusion is realized in an encoding stage.

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 present disclosure relates to a method and a device for predicting the risk of gestational diabetes by using an artificial intelligence model. An embodiment of the present disclosure may provide a method for predicting the risk of gestational diabetes by using an artificial intelligence model, the method comprising the steps of: inputting data on a mother to a gestational diabetes prediction model generated on the basis of a training data set; and obtaining the incidence rate of gestational diabetes from the gestational diabetes prediction model, wherein the data on the mother includes a final variable determined from multiple initial variables of the training data set.

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.