22 results about "Hypertension risk" patented technology

A clinical decision support system and method for patients with pulmonary arterial hypertension is disclosed herein. The system may comprise a processor to process instructions to execute one or more pulmonary arterial hypertension risk algorithms configured to generate a risk score value associated with a patient surviving within a given time period. The system may comprise a means for input and output, wherein input variable data may be received and a set of risk score values may be displayed. A method for operating the clinical decision support system is also disclosed.

The invention relates to the technical field of blood pressure dynamic monitoring systems, and provides an artificial intelligence-based blood pressure dynamic monitoring and risk assessment system, which comprises a patient terminal, a doctor terminal, a data center terminal, a local anomaly detection terminal and a strategy generation terminal, the patient terminal is used for collecting blood pressure, body position change and endocrine hormone fluctuation data of a patient in real time; the data center terminal is used for fusing, storing and processing the data uploaded by the patient terminal and performing overall risk assessment; the local anomaly detection terminal is used for collecting local blood flow rate and pulse wave conduction velocity data and carrying out local anomaly detection index analysis; the strategy generation terminal is used for generating strategy information according to the overall risk assessment result and the local anomaly detection index; and the doctor terminal is used for receiving and displaying the overall risk assessment result and the local anomaly detection index, and receiving and executing the strategy information. According to the invention, the comprehensiveness and timeliness of hypertension risk prediction and intervention are improved.

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.

The invention discloses a primary hypertension risk assessment method based on infrared thermal imaging. The method comprises the following steps: (1) respectively taking two-hand infrared thermograms of a healthy subject, a hypertension patient and a hypertension patient before and after cold stimulation by adopting a medical infrared thermal imager; (2) analyzing hand infrared thermograms before and after cold stimulation of a healthy subject, a hypertensive patient and a hypertensive patient so as to reflect vascular reactivity and blood perfusion states of hands of the healthy subject, the hypertensive patient and the hypertensive patient; and (3) taking hand infrared thermograms of the to-be-tested subject before and after cold stimulation by adopting a medical infrared thermal imager, and obtaining a judgment result according to the vascular reactivity and blood perfusion state of the hand. According to the invention, a medical infrared thermal imager is utilized to shoot infrared thermograms of hands of a subject before and after cold stimulation, so that vascular reactivity and blood perfusion states are visually reflected in a non-contact, real-time and visualized manner, and finally, the purpose of evaluating the risk of primary hypertension is achieved.

An organic sol-gel nano-composition containing three solid lipid nano-components loaded with phytochemical active ingredients is proposed. Panax notoginseng saponins are extracted from Vietnamese Panax notoginseng. Rutin is extracted from Styphnolobium japonicum in Thai Binh Province, Vietnam, and Ginkgo biloba is extracted from Ginkgo biloba trees. The composition has shown to have low toxicity and great bioavailability. In particular, the composition is effective against local myocardial ischemias and arrhythmias, improves the blood rheology, platelet circulation and antiaggregation, and thrombosis, lowers the blood lipids to a certain limit, and has good effects against atherosclerosis. The composition has an antioxidant effect of preventing free radical damages, and has an effect of enhancing capillary resistance, strengthening the vessel walls that reduces hypertension risks, which accordingly protects the cerebral nerves, prevents strokes, and is also used to recover after strokes and other hemorrhagic diseases for the enhancement effects and reconstruction of the damaged blood vessels.

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 application discloses a hypertension early warning model, product, computer readable storage medium and application thereof, and relates to the technical field of biomedical detection. The hypertension early warning or diagnosis marker comprises rs2106809, rs699, rs671 and rs1799983, and the hypertension risk early warning or diagnosis is carried out according to the genotype information of rs2106809, rs699, rs671 and rs1799983 of a sample. The application further constructs an early warning model capable of predicting hypertension. The early warning model can carry out the hypertension risk early warning or diagnosis based on the genotype information of rs2106809, rs699, rs671 and rs1799983. In addition, the early warning model can also combine the data of multiple dimensions such as genes, pulse and BMI, and has the characteristics of low cost, easy acquisition of indexes, comprehensive prediction ability, high accuracy of early warning or diagnosis results and the like.

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.

The invention provides a method and system for evaluating the hypertension disease risk of residents in a residential area under the influence of noise exposure, and belongs to the technical field of disease risk evaluation. The method comprises the following steps: collecting personal basic information, dietary habits, noise exposure level data, building influence factors related to noise propagation and hypertension illness condition data, and carrying out preprocessing and data verification; the hypertension disease risk serves as a dependent variable, other data serves as an independent variable, the variables are assigned, and single-factor logistic regression analysis is conducted on the assigned variables; performing multi-factor logistic regression analysis on the variables with low significance corresponding to the variables; establishing a plurality of evaluation models for the hypertension risk of residents in the residential area under the influence of noise exposure based on a multi-factor logistic regression analysis result, and screening an optimal evaluation model according to the accuracy of a prediction result; and outputting the importance of each risk factor in the optimal evaluation model prediction result. And the hypertension disease risk aiming at the noise exposure influence can be quantitatively estimated.

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 invention relates to a disc tool for rapidly judging hypertension risk layering, and relates to the technical field of medical instruments. The tool comprises four layers of discs, namely a first disc, a second disc, a third disc and a fourth disc which are rotationally connected through a rotating shaft and are gradually increased in size, one side of each layer of disc is equally divided into three areas, and according to the blood pressure value of a patient, the number of risk factors and the target organ damage condition, the first three discs are rotated layer by layer to enable the corresponding areas to face forwards; and the highest dangerous color (red > yellow > green) in the previous three layers is matched with the dangerous grade of the fourth disc. According to the tool, layering judgment is completed within one minute through a mechanical multi-disc linkage and color coding system, the accuracy rate is 100%, the tool is suitable for basic medical institutions and family scenes, the problems of tedious manual operation and long consumed time are solved, and meanwhile the doctor-patient communication efficiency is improved through visual design.

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 discloses an interrogation guidance system based on interactive data collection, which relates to the technical field of interrogation guidance. It includes a data acquisition module, a hypertension feature extraction module, a hypertension risk prediction module, and a guidance adjustment module. By integrating static and dynamic blood pressure data, time series analysis, anomaly detection, and machine learning models, it realizes the accurate identification of white coat hypertension and masked hypertension. It can not only combine the user's physiological data with the characteristics of the measurement environment to improve the accuracy of hypertension risk prediction, but also adjust personalized measurement suggestions through dynamic interrogation guidance and generate targeted blood pressure management plans. This technology effectively reduces the situation of misjudging hypertension due to emotional fluctuations or single measurement, improves the scientific nature of hypertension screening and health management, optimizes the patient's medical decision-making, reduces the risk of unnecessary medical intervention, and thus improves the reliability and clinical application value of the intelligent medical system.

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.

The invention relates to the technical field of medical treatment and health, and discloses a clinical evaluation method for treating hypertension through RDN, which comprises the following steps: S01, acquiring an initial data set of a target user, S02, acquiring an RDN data set of the target user through radiofrequency ablation, and S03, performing hypertension risk analysis according to the initial data set of the target user, s04, performing hypertension risk analysis according to the RDN data set of the target user, S05, comprehensively evaluating the treatment effect of the RDN on hypertension, and S06, outputting an evaluation result to a human-computer interaction terminal to generate a clinical evaluation report, performing risk analysis on the initial data set of the target user, performing risk analysis according to the RDN data set of the target user after radiofrequency ablation, and determining the risk of the target user after radiofrequency ablation. Risk evolution analysis is carried out on the RDN treatment effect through a risk analysis result, and the RDN treatment effect is comprehensively evaluated, so that the risk of drug side effects and drug interaction is avoided.