65 results about "Vascular risk" patented technology

The invention provides a multi-parameter collaborative cardiovascular health monitoring system and method, and the system comprises a millimeter wave radar collection module which is used for collecting a multi-dimensional physiological signal of a target user; the third-stage signal processing module is used for carrying out noise reduction processing on the multi-dimensional physiological signals and carrying out layered feature extraction on the multi-dimensional physiological signals subjected to noise reduction to obtain multi-dimensional physiological feature parameters; and the fourth-order crowd adaptation AI evaluation module is used for carrying out crowd segmentation on the target user to obtain segmented crowds, dynamically adjusting the weight ratio of each feature parameter in the multi-dimensional physiological feature parameters according to the segmented crowds to obtain dynamic adjustment feature parameters, and carrying out blood pressure calculation, angiosclerosis calculation and vascular risk prediction according to the dynamic adjustment feature parameters. And obtaining a blood vessel health monitoring result. According to the embodiment of the invention, through software optimization and measurement technology expansion, the cardiovascular health monitoring accuracy and the application range are further improved.

The invention relates to the field of biosensors, and discloses a cardiovascular data monitoring method for the cardiovascular medicine department. According to the method, electrocardio, respiration and blood oxygen signals are synchronously collected through a non-invasive terminal, sleep apnea events are analyzed and recognized in a combined mode, the electrocardio signals are deeply analyzed to detect cardiovascular abnormalities, and time sequence characterization of the function state of the autonomic nervous system is generated based on heart rate variability; and further constructing a time sequence causal reasoning model, quantifying the triggering effect of the respiratory event on the cardiovascular event, generating a comprehensive night cardiovascular risk layering index, and triggering graded early warning. According to the invention, non-sensitive, long-time-history and high-precision night cardiovascular risk dynamic assessment and causal mechanism analysis are realized.

The invention discloses a traditional Chinese medicine cardiovascular health assessment method based on tongue picture image recognition, and the method comprises the following steps: collecting original tongue picture image data, and carrying out the image preprocessing; collecting lingual surface spectrum original data, and executing spectrum preprocessing; extracting features of the standardized tongue picture image data, and generating tongue picture image feature vectors; extracting features of the standardized lingual surface spectral data, and generating lingual surface spectral feature vectors; constructing multi-modal hidden space representation of the tongue picture image feature vector and the tongue surface spectrum feature vector, and executing feature decoupling and feature fusion processing; establishing a mapping relation between the tongue picture-spectrum fusion implicit vector and a cardiovascular health index, and generating a cardiovascular risk prediction vector; and generating a cardiovascular health risk assessment result. According to the invention, tongue image and spectral analysis are fused, cardiovascular health assessment is realized, and the method has the advantages of high accuracy and high stability.

The invention discloses a cardiovascular risk early warning method based on multi-modal time sequence data, relates to the technical field of medical health information monitoring, and aims to solve the problem of confusion of causes of dyspnea at night by constructing a cross-modal direction and time delay relation in a sliding time window. Phenotype similar phenomena such as pure blood oxygen reduction / wake-up are decomposed into a comparable time sequence interaction structure, so that a blocking chain type process and a non-blocking type heart failure related process can be distinguished on the structural level, and therefore false alarm and missing alarm caused by confusion are reduced. Meanwhile, a multi-channel signal is firstly converted into a time sequence causal diagram, and then in-window statistical characteristics are combined for judgment, so that the model not only utilizes the self change of each channel, but also utilizes the interaction evidence of the first and second channels, the influence direction and the delay length, and the expression ability of the pathophysiological chain difference is improved.

The invention discloses an acute myocardial infarction cardiovascular risk prediction method based on artificial intelligence. The method comprises the following steps: collecting clinical data of a patient and integrating the clinical data into an initial data set; performing cleaning, missing value processing and classification processing on the data initial set, and constructing a standardized feature data set; a competition risk outcome type is defined according to clinical follow-up visit data of the patient, and competition risk label data is constructed; an improved DeepHit model is established; training and cross validation are carried out by using the standardized feature data set and the competitive risk label data, and a training completion model is determined; and predicting the individual competition risk probability of the patient, and generating a risk level and an early warning prompt. According to the invention, individualized dynamic risk prediction is realized, and clinical diagnosis and treatment decision reliability is enhanced.

The invention relates to the field of cardiovascular risk identification, and particularly discloses an intelligent cardiovascular risk assessment system and method. According to the method, multi-dimensional time sequence data in a human physiological system is converted into a dynamic graph capable of explicitly representing the collaborative relation among physiological indexes in each time window, and then a dynamic graph sequence reflecting continuous evolution of a system state is constructed. On the basis, depth feature extraction and fusion of the space-time dimension are carried out on the dynamic graph sequence so as to comprehensively evaluate the evolution trend of the system stability. By capturing the dynamic evolution trajectory of the physiological network topology structure, the scheme can quantify the subtle process of converting the system from a healthy steady state to an unstable state, thereby identifying an early instability signal which is reflected by multi-dimensional relevance drift and is difficult to perceive on a single index, and further realizing system instability risk scoring and asymptomatic risk early warning.

The application discloses a cardiovascular maintenance functional wine based on a composite fermentation process and a preparation method thereof. Through synergistic fermentation of rosewood flavones, five-grain nutrients and rhizoma notopterygii blood-activating components, a cardiovascular maintenance wine with multiple effects of blood pressure regulation, microcirculation improvement, nerve protection and anti-inflammation is developed. Process parameters are optimized and designed to ensure efficient reservation and release of active components, and the cardiovascular maintenance wine is suitable for daily auxiliary conditioning of sub-healthy people and cardiovascular risk groups.

Machine Learning (ML)-based systems and methods are described for predicting cardiovascular disease of users of specific geographic regions. In various aspects, user- specific cardiovascular data of a user may be input into an ML model trained with data of a plurality of cardiovascular risk factors specific to a population of given geographic region. The plurality of cardiovascular risk factors is subdivided into a first training data subset (preselected factors) and a second training data subset (remaining factors). The user-specific cardiovascular data of the user as input into the ML model is data of the user corresponding to the preselected subset of cardiovascular risk factors and the remaining subset of cardiovascular risk factors. The ML model outputs a user-specific cardiovascular prediction of the user. The user-specific cardiovascular prediction comprises a cardiovascular risk score of the user. The cardiovascular prediction is displayed on a graphical user interface (GUI).

Aspects of disclosure relate to a composition and methodology for treating menopause symptoms utilizing a cannabinoid-based nanoplatform composition. The composition includes phytocannabinoids like CBD, CBG, and CBN, alongside isoflavones and polyphenols, all integrated into nanoplatform designed for enhanced bioavailability and controlled release. The formulation is designed to alleviate key menopause-related issues, including vasomotor symptoms (VMS), genitourinary syndrome of menopause (GSM), bone loss, mood disturbances, and sleep disorders, while addressing cardiovascular disease (CVD) risks. The composition combines cannabinoids, such as CBD, with isoflavones and polyphenols, and is incorporated into nanoplatforms for enhanced delivery and efficacy. Additionally, the disclosure includes a kit comprising oral capsules, intravaginal ovules, and instructions for use. The capsules contain a blend of cannabinoids, flavonoids, and polyphenols, while the ovules are composed of CBD and cocoa butter. The kit provides a comprehensive solution for managing menopause symptoms and mitigating cardiovascular risks.

The application discloses a cardiovascular chronic disease data management method based on artificial intelligence, relates to the technical field of artificial intelligence medical management, and comprises the following steps: collecting patient multi-element heterogeneous medical data, normalizing and converting structured and semi-structured data, extracting key features of unstructured medical image data and vectorizing and encoding the key features, and generating a standardized cardiovascular chronic disease comprehensive data cube; calling a pre-trained multi-modal cardiovascular risk assessment model to analyze the data cube, and generating a comprehensive risk assessment report containing a risk level, a contribution factor, an evolution trend and an individualized early warning threshold; combining a clinical path knowledge base to generate an individualized health management plan, continuously collecting plan execution feedback data, dynamically updating the data cube, and iteratively optimizing the assessment report and the management plan. The method realizes multi-element heterogeneous data regularization, enriches risk assessment dimensions, realizes dynamic adaptation of a health management plan, and improves the standardization and individualization level of cardiovascular chronic disease data management.

The application discloses a magnetocardiogram gradient instrument and an electrocardio collection circuit packaging structure and a signal processing system, relates to the technical field of medical equipment, and realizes high-density integration of hardware and reliable protection through a rigid protection frame formed by a circuit chip, a layer plate structure and a connecting assembly, shortens a signal transmission path, reduces the influence of environmental vibration, dust, moisture and electromagnetic disturbance on a synchronous sampling link, and provides a stable data basis for multi-modal fusion imaging. The signal processing system comprises multi-modal synchronous collection, noise suppression, data fusion imaging and a diagnosis auxiliary module, generates a high-precision space-time dynamic imaging atlas by constructing a cardiac electromagnetic field joint forward model and applying time and space double constraints, and outputs a cardiovascular risk assessment, a cardiac arrhythmia positioning and a myocardial ischemia quantification result in combination with machine learning. The application solves the problems of low integration, poor anti-interference and insufficient single modal detection precision of existing equipment.

The application discloses a portable detection kit for assisting in predicting a cerebral vascular risk, a blood sample is processed through a sample processing module and input corresponding test paper, a detection result of the test paper is collected by a biological sensing module, and the detection result is calibrated and temporarily stored through an electronic control module, so that a user can acquire the calibrated detection result through a target device in an NFC touch mode and automatically send the calibrated detection result to a cerebral vascular disease monitoring digital platform, cerebral vascular risk prediction is performed by the cerebral vascular disease monitoring digital platform, and the portable detection kit has the beneficial effects of high portability, simple operation and high detection result accuracy.

The invention relates to a hypertension closed-loop management system and method based on a blood pressure load and a multi-dimensional portrait. The method comprises the following steps: acquiring blood pressure time sequence data of a user; processing the blood pressure time sequence data according to a preset clinical threshold value, and calculating a blood pressure load index BP Burden; according to the blood pressure time sequence data, the blood pressure load index BP Burden and other clinically common blood pressure indexes, a multi-dimensional quantitative evaluation label is generated according to a preset evaluation rule; comprehensively judging the blood pressure management state of the user according to the quantitative evaluation label, and matching a preset intervention strategy knowledge base to generate personalized intervention suggestions; and presenting the quantitative evaluation label, the blood pressure management state and the intervention suggestion to a user or a doctor end, and receiving execution feedback to form closed-loop management, and the method has the advantages that the technical effects of accurate prediction of cardiovascular risks and dynamic optimization of blood pressure control quality are achieved.

The invention provides a multi-modal imaging system fusing physiological parameter monitoring, and the system comprises an ultrasonic collection module which collects ultrasonic detection signals according to a preset time sequence; the physiological acquisition module is used for acquiring time sequence reflecting physiological signals according to a preset time sequence; the feature fusion module is used for carrying out time registration on the ultrasonic detection signals and the time sequence physiological signals, extracting multi-modal features and fusing the multi-modal features to form a joint feature vector; the multi-modal estimation module is used for analyzing and obtaining an imaging result and multi-modal physiological parameters of the target tissue or blood vessel based on the joint feature vector; and the output module is used for outputting the imaging result and the multi-modal physiological parameters. According to the method, ultrasonic detection signals and various time sequence physiological signals are deeply fused, so that global accurate capture of vascular structures, dynamic characteristics and physiological time sequence information is realized, and a full-link closed loop of structural imaging, functional imaging and multi-parameter continuous monitoring is successfully cut through; and a high-precision, multi-dimensional and explainable solution is provided for cardiovascular risk assessment and individualized health management.

The present disclosure provides methods for determining whether a subject has platelet hyperreactivity. Further provided are methods for determining risk of developing a platelet-mediated cardiovascular and / or limb event in a subject, as well as determining whether a subject has a condition associated with platelet hyperreactivity. Still further provided are methods for preventing a cardiovascular and / or limb event in a subject in need thereof; treating or preventing a condition associated with platelet hyperreactivity in a subject in need thereof; and, monitoring the progression of a condition associated with platelet hyperreactivity in a subject diagnosed with the condition. Methods for determining the effect of an antiplatelet treatment on development of a condition associated with platelet hyperreactivity in a subject diagnosed with the condition, and identifying an antiplatelet treatment useful for slowing down the progression or treating a condition associated with platelet hyperreactivity in a subject diagnosed with the condition are also provided.

Apparatus and methods are provided for determining a cardiovascular risk score from at least cardiovascular data. [Solution] The method includes providing a device adapted to measure a cardiovascular signal of a user, measuring the cardiovascular signal using the device during an observation period having a duration of at least 24 hours, each observation period having a duration of 24 hours having a plurality of observation periods, determining a cardiovascular value for each observation period, compiling the determined cardiovascular values ​​for corresponding measurement periods of each observation period into a set of cardiovascular parameters, creating a 24-hour circadian plot of the set of parameters against the corresponding measurement periods, determining a physiological parameter of the user using the circadian plot, and calculating a cardiovascular risk score for the user using the determined physiological parameter.

A device for predicting and diagnosing risk by cardiovascular disease type according to an embodiment comprises at least one processor, wherein the at least one processor predicts risk by cardiovascular disease type using a diagnostic model. The diagnostic model includes a first model that analyzes an inputted fundus image to predict cardiovascular risk; and a second model that receives a cardiovascular risk score predicted by the first model and disease type-specific factors to predict risk by cardiovascular disease type. Wherein the at least one processor is characterized by providing diagnostic information based on the cardiovascular risk predicted by the first model and the risk by cardiovascular disease type predicted by the second model.

This invention discloses a dynamic prediction system for long-term adverse cardiovascular event risk after stent implantation, belonging to the field of personal health risk assessment technology. It includes: a cardiovascular assessment module for determining the target cardiovascular adverse event presentation coefficient based on the target patient's first chest pain rating and real-time heart rate after stent implantation; a cardiac function assessment module for determining the emphasis of cardiac function analysis at each stage based on the target patient's physical examination data after stent implantation; a probability determination module for determining the probability of the first adverse event based on the target cardiovascular adverse event presentation coefficient and the emphasis of each cardiac function analysis; and an indicator determination module for comparing the target patient's first adverse event probability with the second adverse event probability of each monitored patient to obtain the target cardiovascular risk warning indicator for the target patient. This invention can improve the accuracy of long-term risk prediction for adverse cardiovascular events.

The invention provides a cardiovascular emergency multi-parameter intelligent monitoring method and device, relates to the technical field of cardiovascular risk monitoring, and aims to solve the technical problem of poor risk assessment effect in related technologies. The method comprises the following steps: acquiring various physiological signals; carrying out cardiac cycle segmentation and time alignment on the multiple types of physiological signals according to electrocardiograph R wave peak value time to form multi-parameter time coupling data; extracting a first group of biophysical parameters based on the multi-parameter time coupling data; calculating a second group of signal quality parameters based on the original waveforms of the multiple types of physiological signals; constructing a multi-dimensional physiological risk space based on the first group of biophysical parameters; dynamically calculating a spatial risk aggregation degree index based on the real-time or near-real-time data points of the first group of biophysical parameters in the multi-dimensional physiological risk space; determining a cardiovascular emergency risk level identifier based on the spatial risk aggregation degree index and the second group of signal quality parameters; and outputting a cardiovascular emergency risk level identifier.

The present invention relates to GLP-1 / GIP / GCG triple receptor agonists and their use in the treatment or prevention of type 2 diabetes (T2DM), hyperlipidemia / dyslipidemia, metabolic syndrome, metabolic dysfunction-related fatty liver disease (MASLD), metabolic dysfunction-related steatohepatitis (MASH), neurodegenerative disorders, fibrosis, cardiovascular risk and / or obesity.

The invention discloses a health assessment method and system based on continuous blood pressure monitoring of an intelligent ring. The method comprises the following steps: continuously and noninvasively acquiring sensing data of a target user at a plurality of time points through the intelligent ring; calculating a user blood pressure parameter corresponding to the sensing data at each time point based on a preset blood pressure calculation model; according to the user blood pressure parameters corresponding to the at least two time points, health scores of the target user in multiple dimensions are calculated; and determining a cardiovascular health score corresponding to the target user according to the health scores of the multiple dimensions and a preset scoring rule. Therefore, all-weather non-inductive blood pressure monitoring and cardiovascular risk quantitative evaluation can be achieved, the cardiovascular abnormality early-stage discovery capability is improved, and the cardiovascular event risk caused by intermittent measurement or symptom delay awareness is reduced.

The present application relates to the technical field of data transmission, and more particularly to a data transmission method and system of a multifunctional vital sign monitoring system.The method comprises the following steps: collecting first physiological signal data through a sensor arranged in a precordial lead region; collecting second physiological signal data through a sensor arranged in a lower limb or back lead region; aligning the time stamps of the first physiological signal data and the second physiological signal data, and extracting a position calibration vector group; performing compression preprocessing according to the position calibration vector group to obtain a multi-dimensional vital sign compression feature flow; therefore, by constructing a "end-edge-cloud" collaborative vital sign data closed-loop optimization system, the present application solves the problems of spatial information disconnection, high abnormal false alarm rate and threshold rigidity in traditional vital sign monitoring, and improves the accuracy and timeliness of cardiovascular risk early warning in a complex motion scene.

The application is based on a blood vessel risk assessment model system based on a multi-modal fusion strategy and deep learning: an acquisition module acquires examination and test data, image data, gene detection data and clinical data of a plurality of historical patients, and labels various blood vessel disease conditions; a preprocessing module standardizes numerical data, one-hot encodes or entity embedding of category data, converts time series data into fixed length vectors, extracts imaging features from image data and / or learns deep image features from the image data, extracts features from text data, reduces SNPs data, normalizes gene expression profile data and selects features; a feature sample construction module constructs a training set and a validation set using the multi-modal preprocessed features of each historical patient; a model construction module constructs a multi-task deep learning model; a multi-task learning training module trains and validates the model, optimizes the hyperparameters of the model using an optimization algorithm, and adjusts the hyperparameters to the optimal to obtain a target multi-task deep learning model.

The application discloses a glycolipid metabolism index prediction model for predicting cardiovascular events and a construction method thereof. The model aims to solve the problems that existing cardiovascular risk assessment tools are not accurate in describing glycolipid metabolism state and are single in prediction dimension. The construction method comprises the following steps: obtaining demographic information and clinical data of a subject; measuring the content of high-density lipoprotein subcomponent HDL2b; taking body mass index, lipoprotein a, triglyceride-glucose index and HDL2b as core evaluation parameters, taking whether a cardiovascular event occurs within five years as a target variable, and adopting a multivariate logistic regression to construct a prediction model. The model can realize rapid and accurate stratification of the risk of cardiovascular event occurrence of an individual by quantifying index scores and adding up. The application integrates multi-dimensional key indicators of glycolipid metabolism, has the advantages of high prediction performance, clear risk stratification and strong repeatability, and is suitable for early risk screening and evaluation of cardiovascular diseases.

The invention relates to a cardiovascular and cerebrovascular metabolism co-disease risk factor early warning and active health digital intelligence system, and the system comprises a digital intelligence library module, an auxiliary decision module, a digital intelligence management module, a mobile terminal, and a cloud service platform. The mobile terminal and the cloud service platform are used for obtaining the multi-modal data of a user. The auxiliary decision-making module is used for determining user portrait data and a cardiovascular risk level based on the multi-modal data acquired by the mobile terminal and the cloud service platform; the digital intelligent management module is used for generating and outputting an intervention scheme based on the user portrait data and the cardiovascular risk level determined by the aid decision making module; and the digital intelligence library module is used for storing active health knowledge and skill data, behavior and lifestyle intervention skills and generation strategies related to prevention and treatment of hyperglycemia, hyperlipidemia, hypertension, diabetes mellitus, coronary heart disease and cerebral apoplexy. The problem that in the prior art, personalized early warning and auxiliary decision-making services are lacked for cardiovascular and cerebrovascular diseases is solved.

Pulse wave velocity (PWV) is a measure of arterial stiffness and a cardiovascular disease risk factor. Accurate PWV estimation is difficult due to complex arterial dynamics, such as longitudinal motion and natural tissue oscillations. This present disclosure relates to a robust, motion-resistant PWV estimation framework including: 1) tracking and compensating for arterial longitudinal wall motions, 2) suppressing undulating arterial motion via common mode rejection, and 3) enhancing differential signals to extract wall expansion. In vitro experiments with induced lateral motion indicate the framework's PWV estimates (6.19±0.33 m / s) closely matched reference values (6.26±0.12 m / s; error: 1.1%), outperforming methods without motion compensation (4.46±1.78 m / s; error: 28.8%). In vivo trials with five healthy subjects showed an average PWV of 4.18±0.56 m / s using the motion-resistant method, compared to 2.54±0.95 m / s without motion compensation (p<0.005). This framework enhances PWV estimation reliability, offering clinical potential for better arterial stiffness assessment and cardiovascular risk stratification.

The invention relates to the technical field of medicines, and particularly discloses application of 9-anthraformic acid in preparation of a non-addictive peripheral analgesic medicine. It is proved for the first time that 9-anthracene formic acid has a remarkable analgesic effect and can effectively relieve inflammatory pain, the action target is a peripheral part, central side effects such as addiction and respiratory depression of traditional opioid analgesic are avoided, gastrointestinal injury, cardiovascular risk and curative effect capping effect of non-steroidal anti-inflammatory drugs are also overcome, and the 9-anthracene formic acid can be applied to preparation of anti-inflammatory drugs. Animal experiments prove that the analgesic effect of the compound is comparable with that of classical opioid drug morphine, and a brand new candidate drug and an application direction are provided for developing novel non-addiction analgesic drugs.

This provides a method to reduce cardiovascular risk in patients with a genetic profile associated with a response to proprotein converter subtilisin / kexin type 9 (PCSK9) inhibitor therapy. [Solution] A method for treating a patient at risk of major cardiovascular adverse events (MACE) includes: determining the patient's polygenetic risk score (CAD-PRS), which includes a weighted sum of multiple gene variants associated with coronary artery disease; identifying the patient as being at high risk of MACE if the patient has a CAD-PRS greater than a threshold CAD-PRS determined from a reference population; and administering a PCSK9 inhibitor to the patient if the patient has been identified as being at high risk of MACE.

The present application relates to a kind of vascular inflammation prediction method based on knowledge guide and intervention-response drive, belong to artificial intelligence and medical cross technical field.It includes the following steps: acquisition multivariate physiological measurement sequence, completes missing value preprocessing using treatment-aware time series linear interpolation;Physiological and treatment sequence are explicitly decoupled into short-term acute path and long-term cumulative path, respectively through exponential decay model, low-pass filter modeling and are fused by gate mechanism, obtain treatment dynamic representation;Heterogeneous dynamic graph is constructed by fusing medical priori and data learning, executes graph convolution update node representation after treatment modulation adjacency matrix;Finally, the representation of treatment dynamics and graph enhancement is fused, and the TBR prediction value is output by attention weighting and time point specific regression.The present application can effectively separate acute and chronic treatment effects, inject clinical knowledge constraints, and is suitable for tumor survivor cardiovascular risk stratification, vascular toxicity mechanism analysis and personalized diagnosis and treatment management.

This application relates to the fields of medical artificial intelligence and signal processing technology, and provides a cardiovascular risk early warning method and related equipment based on multi-channel pulse imaging and AI. It collects pulse data from multiple arterial regions of a user using intelligent sensors; standardizes the data to generate a pulse time-series matrix; constructs a convolutional neural network based on spatiotemporal attention; extracts spatiotemporal pulse features from the pulse time-series matrix using the convolutional neural network; constructs a health atlas based on the spatiotemporal pulse features and the user's health record simulation graph nodes and edges; generates a cardiovascular risk score for the user based on the feature information in the health atlas; and provides risk warnings based on the risk score. By constructing a health atlas based on multi-channel arterial region pulse data, comprehensively integrating spatiotemporal pulse features to generate a risk score, it comprehensively considers multiple aspects of the cardiovascular system's state, accurately reflects the degree of risk, and provides a basis for early intervention in cardiovascular problem prevention based on this score.