13 results about "Hypertension risk" patented technology

This invention provides a method for hypertension risk assessment, relating to the field of epigenetic detection technology. It includes: performing event alignment and residual modeling on a qualified signal set to establish a base probability at the locus level and forming baseline features through neighborhood consistency screening; constructing a tunneling sensitization model and contextual attention, fusing discriminative and generative evidence to obtain enhanced features; quantifying IGF2BP3 and aligning and fusing it with the enhanced features; constructing an IGF2BP3-mediated metabolic network based on this, performing time-series modeling and stability assessment, and extracting final-state features; fusing the final-state risk vector with multi-gene scores to complete adaptive grading and compliant report generation. This method is robust, interpretable, and easy to deploy.

This invention discloses a method for analyzing hypertension risk factors, applied in the field of computer science. It addresses the problems of existing technologies in analyzing and screening important risk factors for hypertension, such as high requirements for data quality, large manual processing workload, and the inability of analysis results to meet the accuracy requirements of downstream prediction tasks, as well as low operability with high-dimensional data. This invention employs a two-stage approach to screen the set of risk factors causing hypertension. The first stage uses domain knowledge to stratify attributes, then uses mutual information calculation to determine the number of candidate features in each stratum, and then uses mRMR (modular risk factor analysis) to screen the candidate set of risk factors in each stratum. The second stage uses a search agent to search for the optimal set of risk factors in the candidate set. A perceptron is added to sense the quality of the solutions corresponding to the search agent, and a random mutation operator is added to the search agent to perform a secondary screening of features, obtaining the final set of risk factors.

The invention discloses a visual multi-time-point hypertension risk scoring system. The system comprises a patient baseline data parameter and daily health information acquisition module, a project parameter acquisition module, a scoring module, a survival rate calculation module, a graphic display module and a project parameter correction module. The patient baseline data parameter and daily health information acquisition module is used for acquiring general health information, clinical text data, laboratory examination, sleep examination, blood pressure and heart rate of each patient; the project parameter acquisition module is used for acquiring parameters of a to-be-scored project; the scoring module is used for calculating a total risk score of the hypertensive patient; the survival rate calculation module is used for calculating the occurrence risk probability value of the specific adverse cardiovascular event according to the total risk score; the graphic display module is used for displaying the occurrence risk probability of the adverse cardiovascular event and the proportion weight of related risk factors; and the item parameter correction module is used for enabling the user to manually correct the parameter value of the scoring item.

Electrocardiogram-based deep learning for hypertension prediction is described. An electrocardiogram analysis module may include a data preprocessor configured to normalize an electrocardiogram to generate a standardized input for electrocardiogram-based hypertension prediction. The electrocardiogram analysis module may further include a deep learning model including a neural network trained to identify features associated with hypertension from the standardized input and at least one dense layer trained to generate a hypertension risk prediction based on the identified features. The hypertension risk prediction may comprise a probability score indicating a likelihood of hypertension.

Treprostinil or a pharmaceutically acceptable salt or derivative thereof may be used in treating pulmonary hypertension in a subject at risk of developing pulmonary hypertension. The treatment may include administering to the subject a first liquid composition comprising Treprostinil or a pharmaceutically acceptable salt or derivative thereof in a first concentration in aerosolized form for a first treatment period, and administering to said subject a second liquid composition comprising Treprostinil or a pharmaceutically acceptable salt or derivative thereof in a second concentration in aerosolized form for a second treatment period following the first treatment period. The second concentration of the second liquid composition may be higher than the first concentration in the first liquid composition. The liquid compositions are administered using a soft mist inhaler.

The invention discloses a model-based pulmonary arterial hypertension risk prediction method and system, and relates to the technical field of artificial intelligence, and the system comprises a first area obtaining module which is used for obtaining the offline area of a first subject working characteristic curve corresponding to a first clinical prediction model based on a first verification set and a first derivation set; the second area acquisition module is used for acquiring an offline area of a second subject working characteristic curve corresponding to the second clinical prediction model based on the second verification set and the first derivation set; the optimal model determination module is used for determining the second clinical prediction model as an optimal clinical prediction model based on the prediction accuracy; the prediction module is used for inputting the to-be-identified examination data into the optimal clinical prediction model to obtain a pulmonary arterial hypertension risk prediction result corresponding to the to-be-identified examination data, and the problem that the pulmonary arterial hypertension risk of a patient cannot be accurately predicted in the prior art is solved.

The invention discloses a hypertension early-stage behavior sequence abnormity identification and data processing method, and relates to the technical field of data processing, the hypertension early-stage behavior sequence abnormity identification and data processing method comprises the steps that blood pressure related monitoring data and behavior state data of a target patient are acquired from a preset database, and the blood pressure related monitoring data comprise monitoring sequence data of multiple dimensions; on the basis of the difference between abnormal data segments in the blood pressure related monitoring data and a preset normal numerical value range, the abnormal degree value of each abnormal data segment is calculated; based on the abnormal degree value, calculating to obtain an attention range of a preset multi-head attention mechanism; the attention weight is adjusted based on the interaction relation between the behavior state data and the blood pressure related monitoring data, and the final attention weight is obtained; and based on a preset multi-head attention mechanism and the final attention weight, performing identification processing on the blood pressure related monitoring data to obtain a hypertension risk identification result. According to the invention, accurate hypertension risk early warning can be provided.

A hypertension risk assessment method based on time-frequency component mixing guidance, the method comprising: constructing an initial model, wherein the initial model comprises a time component mixing module, a frequency component mixing module, a feature enhancement module, and a prediction module; acquiring an initial data set, wherein the initial data set is a time series data set of a plurality of hypertensive patients; on the basis of the initial data set, training the initial model to obtain a target prediction model; and acquiring target data, and on the basis of the target prediction model, performing prediction on the target data to obtain hypertension risk prediction results of target patients, wherein the target data is hypertension-related data of the target patients, and there are a plurality of target patients. In the hypertension risk assessment method based on time-frequency component mixing guidance, a parameter-free component extraction module and feature enhancement module are designed, thereby effectively reducing network parameters, achieving the effect of decoupling data from a model, and also enabling more accurate prediction of the hypertension risk of patients.

The invention discloses a blood vessel morphological characteristic quantification method based on an eye fundus image and a gestational hypertension detection device. The method comprises the following steps: firstly, carrying out preprocessing and blood vessel enhancement on a collected fundus image, then extracting a blood vessel structure, extracting diameter characteristics, gray intensity characteristics and morphological characteristics of a blood vessel, and judging the type of the blood vessel through a two-stage classification strategy. The first stage performs classification based on a diameter threshold. And for the case that the blood vessel diameter characteristic value is in the middle range boundary, in combination with context information, through constructing a Gaussian probability scoring function, calculating the matching degree score of the blood vessel characteristics and the artery and vein reference characteristics, and realizing two-stage fine classification. And finally, a blood vessel center line topological structure is obtained through a skeletonization extraction algorithm, and the parting dimension and the blood vessel curvature of the blood vessel are calculated in combination with the blood vessel structure. The gestational hypertension detection device uses a result obtained by the blood vessel morphological feature quantification method as a multi-dimensional feature parameter, and predicts a hypertension risk probability by using a classification model.

This invention relates to the field of medical artificial intelligence technology, and discloses a method, system, device, storage medium, and product for predicting the risk of nocturnal hypertension. It employs single-factor analysis combined with clinical relevance to screen several significant research factors. These significant research factors are then input into a nocturnal hypertension risk prediction model for prediction, which reduces fitting risk and noise, improves prediction accuracy, and achieves an optimal balance between predictive performance and clinical operability, facilitating rapid application in busy clinical environments. The nocturnal hypertension risk prediction model of this invention uses a table diffusion model, which can effectively capture the nonlinear relationships between clinical variables, and its predictive performance is significantly better than that of traditional logistic regression models. This invention also uses survival analysis for validation, which not only examines the model's generalization ability and shelf life over time but also effectively corrects for survivor bias caused by time camouflage.

The application belongs to the technical field of medical health monitoring and prevention, and provides a personalized hypertension risk assessment method and system based on big data, relates to the technical field of medical health, comprises the following steps: acquiring physiological indexes and behavior habit data of an individual, constructing a time sequence characteristic matrix, identifying a dependency relationship between risk factors to form a risk propagation graph, screening reference individuals by using graph isomorphism matching, constructing a personalized risk propagation model, and calculating a hypertension incidence risk.The application can accurately identify an individual risk propagation path and a dynamic law, realize early personalized early warning of hypertension risk, and improve the accuracy and effectiveness of prevention intervention.

The application discloses a hypertension early behavior sequence anomaly identification and data processing method, relates to the technical field of data processing, and comprises the following steps: acquiring blood pressure related monitoring data and behavior state data of a target patient from a preset database, wherein the blood pressure related monitoring data comprises monitoring sequence data in multiple dimensions; calculating abnormal degree values of each abnormal data segment based on the difference between the abnormal data segment in the blood pressure related monitoring data and a preset normal value range; calculating the attention range of a preset multi-head attention mechanism based on the abnormal degree values; adjusting the attention weight based on the interaction relationship between the behavior state data and the blood pressure related monitoring data to obtain a final attention weight; and identifying and processing the blood pressure related monitoring data based on the preset multi-head attention mechanism and the final attention weight to obtain a hypertension risk identification result. The application can provide accurate hypertension risk early warning.

The present invention relates to the field of disease prediction, and in particular, to a gastrointestinal electrical signal-based diabetes and hypertension prediction system and a construction method. The prediction system provided by the present invention comprises: a database, used for storing gastrointestinal electrical signal data, comprising the postprandial coupling percentage at the lesser curvature lead, the postprandial electrical dysrhythmia percentage at the gastric antrum lead, the preprandial dominant frequency at the lesser curvature lead, the postprandial average waveform frequency at the ascending colon lead, the postprandial normal slow wave percentage at the ascending colon lead, the postprandial coupling percentage at the descending colon lead, the postprandial average waveform frequency at the rectal lead, the preprandial average waveform frequency at the transverse colon lead, and the preprandial lead time difference at the rectal lead; and a prediction module, used for predicting a probability of the occurrence of diabetes and hypertension in a subject. Further provided in the present invention is a construction method for the prediction system. The present invention is adapted to performing diabetes and hypertension risk prediction and screening work in rural or community populations.