155 results about "Vascular risk" patented technology

The invention relates to the technical field of health status assessment, in particular to a cardiovascular disease layered early warning system, which is characterized in that blood vessel compliance and pulse wave velocity are extracted, arterial dilation capacity is measured, blood vessel elasticity is calculated, blood perfusion distribution is detected, and abnormal vasoconstriction is classified on the basis of a hemodynamic state and a heart electrical activity state; in combination with the thickness of the blood vessel wall and the blood vessel stenosis rate, the plaque deposition condition is analyzed, the arteriosclerosis score is calculated, the thrombosis tendency is detected, the local blood flow blocking degree is quantified, the isolation forest algorithm is adopted, the blood flow dynamic change is calculated, the heart rhythm abnormality is analyzed, the acute vascular occlusion probability is measured, and the sudden heart rhythm abnormality risk is evaluated. And based on a gradient boosting tree algorithm, screening high-risk cases, matching medical intervention measures, setting a risk buffer mechanism, adjusting an individual health management strategy, and optimizing a cardiovascular risk hierarchical early warning system.

The invention relates to the technical field of cardiovascular monitoring, in particular to a cardiovascular data monitoring method for the cardiovascular medicine department, which comprises the following steps of: performing sparse regularization processing on characteristic parameters of blood pressure waveform and pulse wave velocity based on a variable set of cardiovascular high-dimensional data; according to the method, redundant variables in high-dimensional data are optimized through sparse regularization processing, a dependency matrix is constructed to extract weight distribution, the accuracy and efficiency of data processing are improved, deep integration of multi-source data is achieved based on variable relevance optimization and distribution parameter updating, the fine analysis capacity of dynamic signals is remarkably enhanced, and the method is suitable for large-scale popularization and application. Through time sequence feature extraction and anomaly labeling, accurate positioning of an abnormal interval is improved, the distribution rule analysis effect of electrocardiosignals is enhanced, dynamic change rate calculation and fluctuation error correction of the pulse wave speed are combined, the reliability of dynamic trend prediction is guaranteed, extraction and evaluation of key risk factors are further enhanced, and the reliability of dynamic trend prediction is improved. And dynamic assessment and accurate quantification of cardiovascular risks are realized.

The invention relates to the technical field of cardiovascular medicine, in particular to an automatic alarm system for cardiovascular medicine, which comprises a biomarker analysis module, a physiological parameter analysis module, a cardiovascular risk assessment module, a cardiovascular early warning adjustment module and an alarm trigger notification module. According to the invention, by integrating real-time monitoring of blood biomarkers and cardiovascular physiological parameters, the early recognition capability of cardiovascular disease risks is improved, key biomarkers are dynamically measured and compared with historical marker data, and subtle physiological changes can be captured at the early stage of asymptomatic conditions, so that potential cardiovascular events are warned; through correlation analysis of electrocardiogram, blood pressure and pulse waveforms, the system can accurately draw a comprehensive image of cardiovascular functions, so that more targeted monitoring and intervention are implemented, timeliness and accuracy of medical response are ensured, doctors can formulate targeted monitoring schemes according to real-time data, and the monitoring efficiency is improved. Therefore, deterioration or occurrence of cardiovascular events of patients can be effectively prevented.

The invention discloses a coronary artery calcification early warning system for type 2 diabetes patients, and relates to the technical field of medical detection. A data acquisition module is used for acquiring continuous physiological parameter data of a user; the risk modeling module is combined with coronary artery calcification evolution characteristics in historical clinical samples to construct a multi-parameter dynamic association model; an index weight calculation unit generates a risk influence factor vector based on a sensitivity analysis result of the physiological indexes on risk prediction; the machine learning analysis module performs iterative training on the prediction model by adopting an integrated learning algorithm, and performs prediction updating by utilizing a risk influence factor vector; the early warning trigger module dynamically generates a graded early warning signal according to the grading trend and a set threshold value; the weak item positioning module carries out contribution degree analysis and anomaly recognition on the key risk indexes and automatically generates personalized intervention suggestions; according to the invention, early recognition and dynamic early warning of coronary artery calcification progress can be realized, and the method is suitable for intelligent early warning management scenes of chronic disease cardiovascular risks.

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 provides a blood pressure measuring method, wearable equipment and a storage medium. The method is applied to the wearable equipment, the wearable equipment comprises a blood pressure measuring device, and the blood pressure measuring device comprises an air bag. In the first time period, the wearable device inflates the air bag and obtains a first pulse wave signal, the air pressure in the air bag is gradually increased along with time in the first time period, and the first pulse wave signal is used for obtaining a first blood pressure measurement value; in a second time period, the wearable device inflates the air bag and obtains a second pulse wave signal, the air pressure in the air bag is kept unchanged or fluctuates in the first range in the second time period, and the second pulse wave signal is used for evaluating the cardiovascular risk; the wearable device outputs a first blood pressure measurement and cardiovascular risk. By means of the method, the wearable device can monitor the cardiovascular disease risk of the user while obtaining the peripheral arterial blood pressure value, and cardiovascular diseases are prevented.

The invention discloses a heart tension index dynamic calculation and risk assessment method based on HRV, and the method comprises the steps: carrying out the filtering and denoising of an electrocardiosignal through a Butterworth filter, and obtaining a filtered electrocardiosignal; calculating interval data of adjacent wave crests; processing the interval data according to fast Fourier transform to obtain frequency domain features, and combining the interval data to obtain time domain features; performing normalization processing on the time domain and frequency domain features based on a maximum and minimum normalization method; calculating a cardiac tension index according to the normalized features and a pre-trained multiple linear regression model; matching a pre-constructed reference database according to the individual age, gender and body mass index to obtain a reference cardiac tension index; calculating a cardiovascular risk score in combination with the normalized frequency domain features and a reference cardiac tension index; and repeating the above steps regularly, smoothing through a moving average method based on continuous multiple cardiovascular risk scores, and analyzing the trend to send out cardiovascular risk early warning information.

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 relates to the technical field of computer system development, and discloses a worker cardiovascular and cerebrovascular risk grading evaluation method based on prior knowledge, comprising the following steps: S1, generating a unique code; s2, judging whether a miner needs to be evaluated or not; obtaining primary screening information for the to-be-evaluated objects; s3, judging whether the miner meets the standard or not; if yes, there is no risk; entering S6; otherwise, entering S4; s4, obtaining secondary screening information and a secondary health score; s5, judging whether the risk is abnormal or not, if not, recording the risk as low risk, and entering S5; if not, entering S6; S5, obtaining third-level screening information and a third-level health score; s6, determining whether there is an abnormality, and if there is no abnormality, recording the risk as a middle risk; otherwise, refusing to go down the well and marking the miner health risk level as a high risk; s6, downhole operation is allowed, and the risk level is marked; S6, downhole operation is allowed, and the risk level is marked; through three times of screening, the screening speed is increased, and the screening cost is reduced.

The invention discloses a hog cholera virus detection system based on cloud analysis, which comprises an image acquisition unit, a data processing cloud, a disease monitoring unit and an early warning management unit, and is characterized in that a live pig limb image is acquired through the image acquisition unit, and an image analysis model is constructed through the data processing cloud to analyze the live pig limb image; the hog cholera risk suspected degree is comprehensively evaluated from two perspectives of vascular risk and tissue edema, so that auxiliary diagnosis is carried out on hog cholera virus detection; the disease monitoring unit is used for monitoring the hog cholera virus for a long time, the deterioration or recovery condition of the disease condition of the sick pig is analyzed, a sick pig health management scheme is generated, and the targeted treatment effect of the sick pig is improved; and the early warning management unit globally analyzes the virus risk and generates a live pig breeding management scheme, so that corresponding overall management and control are performed for the hog cholera virus infection degree of the live pig breeding base, the early warning management efficiency of the live pig breeding base is improved, and the live pig breeding loss and the management cost are reduced.

The invention discloses a cardiovascular medicine nursing remote monitoring system, and relates to the field of medical information technology and remote medical technology, the cardiovascular medicine nursing remote monitoring system comprises a remote electrocardio monitoring center, the remote electrocardio monitoring center is in communication connection with a cardiovascular data acquisition module, a cardiovascular risk prediction module and a cardiovascular health visualization module; the cardiovascular data acquisition module collects physiological data through a wearable sensor, the cardiovascular risk prediction module predicts the cardiovascular disease risk by using an elastic network regression analysis model and provides personalized suggestions, and the cardiovascular health visualization module displays the health trend and updates cloud data in real time. According to the remote nursing monitoring system for the cardiovascular medicine department, through real-time monitoring and data analysis, the precision and efficiency of cardiovascular disease management are improved, optimal configuration of medical resources is promoted, individuation and continuity of health management of patients are enhanced, communication and cooperation among medical teams are enhanced, and the medical management efficiency is improved. And higher-quality remote medical service is provided for the patient.

The invention discloses a pregnancy risk auxiliary management method and system based on cardiovascular diseases, and relates to the technical field of medical auxiliary management. The pregnancy risk auxiliary management system comprises a system management module, an evaluation module, a follow-up visit management module, an information interaction module and a support database. The auxiliary management system can automatically generate targeted pregnancy risk assessment results and follow-up management suggestions according to related basic data of patients, so that the assessment and management efficiency of doctors and pharmacists on cardiovascular risks of patients receiving ART can be remarkably improved, and the follow-up management time is greatly saved; the accuracy, comprehensiveness and reliability of follow-up management suggestions can be remarkably improved, timely and effective cardiovascular disease related risk follow-up management suggestions can be provided in the pregnancy process, and the method is suitable for large-scale pregnancy risk auxiliary management based on cardiovascular diseases.

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.

A pharmaceutical composition comprising: (a) from about 1 to about 25 wt% AZD0780; (b) from about 60 to about 95 wt% of two or more pharmaceutical diluents; (c) from about 3 to about 5 wt% of one or more pharmaceutical disintegrants; (d) from about 0.5 to about 3 wt% of one or more pharmaceutical lubricants; (e) from 0.0 to about 5 wt% of one or more pharmaceutical glidants; and (f) from 0.0 to about 5 wt% of one or more pharmaceutical binders, wherein the component weights add up to 100 and a method of lowering LDL-C levels, reducing cardiovascular risk and / or treatment of a cardiovascular disease, comprising administering to a subject in need thereof AZD0780, or a pharmaceutically acceptable salt thereof, in a daily dose of from 1 mg to 30 mg.

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).

The invention relates to a CVD risk assessment tool based on wearable device data and a machine learning algorithm, and the tool comprises a data collection module which obtains cardiovascular traditional risk factors through a cardiovascular risk assessment scale, and obtains monitoring data through a wearable device; the feature screening module is used for determining a candidate predictive factor range, and then performing feature extraction and predictive factor screening on the crowd wearing the wearable equipment by jointly using a minimum absolute contraction and selection operator (LASSO), a random forest (RF) and a Logistic regression model; the model building module is used for building a model based on an XGBoost machine learning algorithm; and the risk diagnosis module is used for outputting and evaluating the probability that the CVD risk of the individual is high within 10 years based on the characteristic data obtained by the wearable equipment data in real time. The cardiovascular health condition of an individual can be dynamically evaluated, personalized and timely risk early warning is provided, the method is suitable for daily health management, remote monitoring and other scenes, and a new solution is provided for early intervention and personalized medical treatment of cardiovascular diseases.

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 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 invention relates to the technical field of medicinal chemistry, in particular to application of a compound Resinacein S or a derivative thereof in preparation of a medicine for preventing or treating obesity or related metabolic complications. The invention also provides an application of the compound Resinacein S or the derivative thereof in preparation of medicines for preventing or treating obesity or related metabolic complications. Researches find that the compound Resinacein S has the effect of preventing and treating obesity, the action mechanism is that fat accumulation is reduced by increasing energy metabolism of the whole body, the effects of reducing body weight and preventing and treating obesity are achieved, and compared with a traditional weight-reducing medicine for reducing body weight by inhibiting appetite, the compound Resinacein S does not prevent and treat obesity by interfering with appetite, and the effect is obvious. And side effects such as gastrointestinal discomfort and cardiovascular risk caused by appetite intervention are avoided.

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.

The invention relates to the technical field of medical care, in particular to a cardiovascular disease risk dynamic early warning system based on multi-source data fusion, and the system comprises the steps: obtaining ejection fraction, coronary artery stenosis percentage and calcification integral data of cardiovascular examination of a monitored object, and heart rate data and pulse pressure difference data of periodic signs; determining a physical examination abnormal coefficient according to the difference between the ejection fraction and the standard value and the difference between the calcification integral data and the standard value and the coronary artery stenosis percentage; according to the height and slope difference of the ST wave band and the PR wave band, the heart rate analysis abnormal index is determined by combining the time interval of the adjacent electric activity wave bands; according to the time sequence change correlation of the heart rate data and the pulse pressure difference data, a physical sign abnormal coefficient is determined; determining a dynamic risk score in combination with the physical examination abnormal coefficient and the physical sign abnormal coefficient; and carrying out risk early warning based on the dynamic risk score. According to the invention, fusion analysis of multi-source and multi-dimensional data can be realized, and the dynamic early warning precision of cardiovascular risks is improved.

The present invention relates to the field of medical prediction, and provides a system for building an identification model for metabolically unhealthy obesity, a storage medium, and a kit. The identification model building system includes: an acquisition module, a feature selection module, a training module, and an identification module. Through machine learning of plasma metabolite biomarkers, it is possible to identify patients with metabolically unhealthy obesity early before clinical symptoms appear, helping doctors and patients take intervention measures before cardiovascular risks increase. The identification model building system for metabolically unhealthy obesity of the present invention is simpler than the traditional physical examination process, reducing the burden of patient participation.

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 invention discloses a resection device, and relates to the technical field of medical instruments. The excision device comprises a catheter, a cutting assembly and a recycling assembly, the cutting assembly and the recycling assembly are arranged at the far end of the catheter, the recycling assembly comprises a suction part and a flushing part, the suction part is at least partially connected to the far end of the catheter and provided with at least one suction opening used for adsorbing a target object, and the flushing part is connected to the far end of the suction part and provided with at least one suction opening used for adsorbing the target object. And at least one spraying opening for spraying flushing fluid is formed. The excision device is provided with the recycling assembly, flushing fluid is sprayed through the flushing part of the recycling assembly to conduct thrombolysis, meanwhile, thrombus is recycled through the suction part, the risk that the thrombus falls off and blocks a far-end blood vessel is reduced, and therefore the possibility of occurrence of far-end embolism complications is reduced; and the resection device can be used for resection of plaques with mixed lesions.

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.