71 results about "Stroke risk" patented technology

The invention relates to the technical field of intelligent medical systems, and discloses a cerebral apoplexy onset risk assessment and reminding method and system.The method comprises the steps that continuous medical structured detection data are collected, and the data comprise carotid artery blood flow parameters, brain oxygen saturation, heart rate variability and metabolic indexes; inputting a bidirectional LSTM, a differential convolutional network, a wavelet residual network and a multi-layer perceptron to extract nonlinear features; constructing a neural function coupling structure diagram of four nodes of cerebral blood supply, oxygen supply, autonomous regulation and metabolic steady state; calculating inter-node time sequence offset correlation and a stable factor to obtain a coupling anomaly coefficient; and driving the embedded network by using a graph structure and a node feature input mechanism, and outputting a risk state assessment result. According to the method, the neural function coupling structure diagram is constructed and the mechanism is introduced to drive the embedded network, so that high-precision identification of the multi-system collaborative abnormal state and dynamic evaluation of the stroke risk level are realized.

The invention discloses a cerebral apoplexy multi-mode early screening intelligent evaluation system based on a large model, and relates to the technical field of medical health information, the cerebral apoplexy multi-mode early screening intelligent evaluation system comprises an intelligent management platform, and the intelligent management platform is in communication connection with the following modules: a multi-source heterogeneous data fusion engine, the data integration module is used for integrating multi-modal data including clinical data and terminal health data and constructing a health portrait of a patient; and the cerebral apoplexy knowledge graph construction platform is used for constructing a cerebral apoplexy domain knowledge graph in combination with evidence-based medical knowledge. By combining the digital twinning technology and the intelligent risk assessment engine, the influence of different intervention schemes on the cerebral apoplexy risk can be simulated, personalized intervention suggestions are generated, a patient is helped to reduce the cerebral apoplexy risk and change from passive prediction to active intervention, the patient is helped to take effective measures earlier, the health condition is improved, and the patient experience is improved. The occurrence of cerebral apoplexy is prevented, so that the disability rate and the death rate caused by cerebral apoplexy are reduced.

The invention relates to the technical field of medical prediction, in particular to a Web-based big data driven stroke risk prediction method, which comprises the following steps: S1, multi-scale data acquisition and alignment: acquiring physiological data, behavior data and health state data of a user; s2, causal path identification: generating a causal feature sub-graph with confidence rating; s3, feature fusion modeling: performing response modeling on fluctuation features in the real-time physiological data, performing trend feature extraction on behavior data, and dynamically allocating fusion weights of the fluctuation features and the trend features to generate unified risk characterization features; s4, risk evolution simulation: simulating the influence of different intervention strategies on the stroke risk, and generating a risk evolution trajectory; and S5, intervention scheme generation: generating a personalized intervention scheme. According to the invention, the accuracy of risk prediction is improved, and the change of the health condition of the patient can be responded in real time.

The invention relates to the technical field of image recognition, and discloses a carotid plaque stability and stroke risk prediction system fusing multiple modes, and the system comprises a data collection and fusion module, a feature extraction and association module, a risk assessment and layering module, an intervention decision module, and a report generation and feedback module. When carotid plaque stability and cerebral apoplexy risk prediction is carried out, plasma proteomics, radiomics and clinical data are synergistically integrated by establishing a multi-modal data fusion analysis framework, so that the limitation that a traditional method depends on a single data source is overcome; the plaque risk can be evaluated from multiple dimensions of biological activity and morphological features, the comprehensiveness and accuracy of risk prediction are improved, a more reliable diagnosis basis is provided for clinic, interaction and sensitivity influence among different modal features can be adaptively quantified by introducing dynamic feature correlation modeling and a real-time weight calibration mechanism, and the risk prediction accuracy is improved. And objectivity and consistency of risk assessment results are ensured.

The invention discloses a multi-modal iconography evaluation method suitable for atrial fibrillation cardiac stroke. The method comprises the following steps: acquiring and preprocessing heart and brain multi-modal image data; carrying out multi-modal image registration on the preprocessed data; feature extraction and fusion are carried out on the registered heart and brain multi-mode image data; performing cross-modal feature alignment and fusion on the heart and brain image data; constructing a heart and cerebral vessel integrated evaluation interaction model based on a graph neural network; predicting the risk of the end-to-end cardiac stroke; according to the multi-modal iconography evaluation method, multi-modal heart image data and multi-modal brain image data are combined, a heart and cerebral vessel integrated evaluation framework is established, image features and clinical data are fused through an artificial intelligence method, and end-to-end stroke risk prediction and evaluation are achieved. According to the method, artificial intelligence and medical technologies are comprehensively utilized, and the accurate cardiac stroke assessment method is provided.

The invention discloses a cloud edge collaborative early warning method for early recognition of cerebral apoplexy, and the method comprises the steps: collecting the posture data and voice data of an edge end, and carrying out the preprocessing of the data; on the edge end, lightweight model reasoning is performed on the attitude data and the voice data based on a sub-modal processing and feature fusion strategy, and finally the cerebral apoplexy risk probability is output; and cooperative and multi-terminal linkage early warning is carried out based on the cloud and the edge terminal. According to the method, active capture of the cerebral apoplexy risk is realized through a framework of edge-end multi-modal real-time processing, cloud collaborative optimization and multi-end linkage early warning, the mode is converted from passive help calling to active early warning-rapid linkage, and the early warning precision and the response speed are improved.

The invention discloses a multi-modal feature integrated risk assessment method and system for a stroke dangerous group, and relates to the technical field of telemedicine collaboration, a telemedicine collaboration network platform is built, and multi-modal data of a stroke high-risk group is collected and processed; analyzing the relation between risk factors and a cerebral apoplexy pathological mechanism by applying bioinformatics and medical knowledge, and defining a key action path; and based on an analysis result of the key action path, integrating multi-modal data by taking a pathological mechanism as an axis, and constructing a dynamic risk knowledge network based on a knowledge graph. According to the method, multi-modal data are integrated, a comprehensive patient individual feature matrix is constructed, a dynamic risk knowledge network is combined, and the association between patient individual features and a cerebral apoplexy pathological mechanism and the dynamic change of risk factors are accurately analyzed, so that an accurate risk score is calculated, the cerebral apoplexy risk of a cerebral apoplexy risk crowd is analyzed, and the cerebral apoplexy risk of the cerebral apoplexy risk crowd is analyzed. A powerful basis is provided for prevention of the cerebral apoplexy, and the occurrence rate of the cerebral apoplexy is reduced.

The invention discloses a potential stroke risk assessment method and system after coronary artery recanalization, and the method comprises the steps: obtaining the heart and cerebral vessel image data of a patient, and constructing a complete heart and cerebral artery blood vessel geometric model; based on the cardio-cerebral artery blood vessel geometric model, numerical simulation is conducted on the flowing conditions of blood flow in coronary arteries and cerebral vessels by means of a computational fluid mechanics method, and the pressure ratio before and after coronary artery stenosis and the partial pressure ratio of the cerebral vessels are calculated; according to the pressure ratio and the partial pressure ratio, the potential risk of the cerebral arterial thrombosis is quantitatively evaluated, and a cerebral blood flow partial pressure ratio numerical value and a cerebral arterial thrombosis risk evaluation report are generated. According to the cerebral apoplexy risk quantitative evaluation method, by integrating coronary artery CTA and cranial CTA or MRA data, reconstructing a complete heart and cerebral vessel geometric model and simulating heart and cerebral blood flow changes before and after a coronary artery recanalization operation, and particularly by calculating coronary artery FFR and cerebral blood flow partial pressure ratio FPR, an evaluation report is generated, and a basis for quantitative evaluation of the cerebral apoplexy risk in the operation is provided for clinical doctors.

The invention provides a medical data processing method and system and related equipment, and relates to the technical field of medical data processing. The method comprises the steps that multi-modal medical data of a target object are acquired, and the multi-modal medical data comprise at least one of clinical medical data, medical image data and biomarker data; inputting the multi-modal medical data into a multi-modal feature extraction model, and outputting multi-modal feature data related to intracranial atherosclerotic stenosis (ICAS) stroke risk prediction; and inputting the multi-modal feature data into a pre-trained ICAS stroke risk prediction model, and outputting an ICAS stroke risk prediction result of the target object. According to the invention, deep integration and accurate feature mining of multi-modal medical data can be realized, the efficiency and accuracy of ICAS stroke risk prediction are significantly improved, and decision support is provided for a clinician to make a personalized treatment scheme.

The invention discloses a paralytic nursing intelligent early warning system and method based on the Internet of Things technology. The system comprises a multi-modal physiological parameter acquisition module, an edge calculation preprocessing unit, an intelligent data transmission module, a multi-scale time sequence feature extraction module, a space-time diagram convolutional network module, a cross-modal attention fusion module, a multi-task risk prediction module and a model training and optimization module. According to the system, multi-mode data such as electrocardio, blood pressure, blood oxygen, eye movement tracks and voice are collected, preprocessed at an edge end and then transmitted to a cloud end; a multi-scale convolutional network is adopted to extract time sequence features, parameter association is modeled through space-time diagram convolution, cross-modal data fusion is realized by using an attention mechanism, and finally risk classification, anomaly detection and trend prediction are completed through a multi-task network. The early-stage, accurate and explainable early warning of the stroke risk is realized, and the early warning accuracy and clinical practicability are remarkably improved.

The invention relates to the field of medical imaging and clinical data machine learning, and discloses an ICAS stroke risk prediction method and system, and the method comprises the steps: carrying out the preprocessing of image data; clinical data such as past medical history, clinical symptoms and family history of the patient are collected; analyzing related biomarkers of the blood sample to obtain biomarker data, and selecting variables closely related to the stroke risk; then carrying out feature splicing to form a multi-modal fusion feature vector; and finally, training, verifying and testing by adopting a machine learning algorithm, and constructing to obtain an ICAS stroke risk prediction model. And based on the ICAS stroke risk prediction model, generating a stroke risk score for each patient, and generating a clinical decision support strategy. The performance of the stroke risk prediction method and system can be improved continuously, accurate risk prediction can be provided in different clinical environments, and accurate stroke risk assessment and targeted treatment schemes can be provided for ICAS patients.

The invention provides a stroke early warning system fusing traditional Chinese medicine characteristics, western medicine indexes, psychological states, video behaviors, voice voiceprints and traditional Chinese and western medicine inquiry information, and the system comprises a traditional Chinese medicine characteristic analysis module which carries out the weighted calculation of a stroke risk score based on the quantitative scores of a face image, eye diagnosis, a tongue image and a pulse image; the western medicine index analysis module is used for calculating a stroke risk score based on the electronic medical record, the family medical history and the past medical history; the psychological state analysis module is used for calculating a stroke risk score through characteristics such as low emotion; the video behavior analysis module is used for calculating a stroke risk score through behavior characteristics such as tumble; the voice voiceprint analysis module is used for calculating a stroke risk score through characteristics such as aphasia; the traditional Chinese and western medicine inquiry analysis module is used for calculating a stroke risk score according to symptoms such as dizziness; and finally, the six types of risk scores are integrated through a comprehensive evaluation module, and accurate layered early warning is realized.

The invention provides a medical data processing method and system based on a large model and related equipment, and relates to the technical field of medical data processing. The method comprises the following steps: acquiring multi-modal medical data of a target object; performing feature extraction and fusion on the multi-modal medical data; inputting the multi-modal fusion feature data into a pre-trained intracranial atherosclerotic stenosis (ICAS) stroke risk prediction model, and outputting ICAS stroke risk prediction results of the target object in a plurality of time periods; generating a personalized rehabilitation scheme of the target object according to the ICAS stroke risk prediction result of the target object in each time period based on a medical reasoning agent system driven by a large language model; and obtaining state data and feedback data of the target object in each time period, and dynamically optimizing an inference strategy of the medical inference agent system according to the state data and the feedback data of the target object in each time period. According to the invention, refined quantification and full-period intelligent management of the stroke risk of the ICAS patient can be realized.

The invention relates to the technical field of medical image early warning, and discloses a cerebral apoplexy early warning method based on a cerebrovascular image representation group. The method comprises the following steps: acquiring a cerebrovascular image sequence, and constructing multi-modal information containing a vascular structure and hemodynamics; based on this, a multi-level representation network is constructed, and a dynamic time-varying risk representation graph is generated through node interaction and space-time modulation. Then performing multi-scale grid segmentation on the risk map to obtain a plurality of local evaluation units, and synchronously screening out candidate units with abnormal evolution according to a preset risk dynamics model; and aggregating the candidate unit features to generate a global risk evolution path, backtracking and positioning to a high-risk frame set in the original image, and outputting an early warning signal after feature decoupling and recombination. According to the method, quantitative tracking and early-stage accurate positioning of the dynamic evolution process of the stroke risk in the space-time space are realized.

The invention discloses a cerebral apoplexy methylation detection kit and application thereof. The kit contains specific reagents, namely a PAX5 PCR reaction solution, a BRCA1 PCR reaction solution, a MACO1 PCR reaction solution and a C1orf232 PCR reaction solution, which are used for respectively carrying out methylation detection on cerebral apoplexy related markers PAX5, BRCA1, MACO1 and C1orf232 in a detection sample on the basis of a fluorescent quantitative PCR (Polymerase Chain Reaction) technology; the kit also comprises a positive reference substance and a negative reference substance. The four target genes PAX5, BRCA1, MACO1 and C1orf232 in the detection sample are subjected to methylation joint detection, so that whether the sample has the cerebral apoplexy occurrence risk or not is judged, the difference possibly caused by site selection and a reaction system is effectively supplemented, the randomness of base change is reduced, and the accuracy of cerebral apoplexy risk prediction is improved.

A stroke detection system operative to detect strokes suffered by mobile communication device users, the system comprising: a hardware processor operative in conjunction with a mobile communication device having at least one built-in sensor; the hardware processor being configured to, typically repeatedly and typically without being activated by the device's user, compare data derived from the at least one sensor to at least one baseline value for at least one indicator of user well-being, stored in memory accessible to the processor, and / or make a stroke risk level evaluation; and / or perform at least one action if and only if the stroke risk level is over a threshold.

The application relates to the technical field of electrocardiosignal processing, and discloses a monitoring electrocardio data analysis method based on online monitoring, wherein the method comprises the following steps: performing multistage filtering pretreatment on an original electrocardio signal; detecting a QRS complex and extracting an RR interval sequence; extracting time domain statistical features, frequency domain features and wavelet coefficients to form an electrocardio feature vector; inputting the electrocardio feature vector and an electrocardio signal segment into an atrial fibrillation detection model containing a signal feature learning branch and a sequence feature learning branch, and outputting an atrial fibrillation detection result; and generating an alarm information based on the detection result and pushing the alarm information to a medical terminal. Through atrial fibrillation load statistics and stroke risk grade evaluation, the single abnormality detection result is expanded into a continuous risk quantization index, a coherent analysis link from abnormality identification to risk evaluation is provided for clinical doctors, complete data storage is associated with a patient identifier and a time stamp, all analysis processes can be traced back, and the needs of clinical audit and department review are met.

The invention relates to the technical field of medical treatment, and discloses a construction method of a left auricle hemodynamics simulation system, which comprises the following core steps of: firstly, acquiring noninvasive transthoracic echocardiography and cardiac enhancement CT (Computed Tomography) image data of a patient; secondly, through an image fusion technology, an accurate left auricle three-dimensional geometric structure model is constructed; and finally, on the basis of the model, carrying out hemodynamic simulation on the left atrium and the left auricle by utilizing a CFD (Computational Fluid Dynamics) technology, and accurately calculating hemodynamic parameters such as the blood flow velocity at the opening of the left auricle. The method has the advantages that the blood flow state of the left atrial appendage is evaluated completely, noninvasively, accurately and quantitatively, and traditional invasive transesophageal ultrasonic examination can be effectively replaced. According to the method, the comfort level of the patient and the examination safety are remarkably improved, operation related risks are avoided, meanwhile, the screening feasibility and efficiency are improved, and the method has important value for clinical management of the stroke risk of the atrial fibrillation patient.

Medical Sensor Device for predicting Hypertension and Stroke Risks

This invention, based on the screening and identification of environmental exocomics and metabolomics biomarkers associated with ischemic stroke, discloses an ischemic stroke risk assessment model and model construction method that integrates environmental exocomics and metabolomics biomarkers, improving the comprehensiveness, accuracy, and convenience of prediction. This invention systematically evaluates the association between exposure to mixed organic pollutants and the risk of ischemic stroke, identifying key pollutants; quantifies the impact of pollutant mixtures on the systemic metabolic network and stroke risk, revealing the mediating role of metabolic pathway disorders in pollutant-induced stroke risk; explores the potential of incorporating pollutant exposure information into clinical prediction models to improve the efficacy of ischemic stroke risk stratification, ultimately providing systematic multi-omics evidence for the causal relationship between environmental pollution and ischemic stroke, and offering new technical tools for precision disease prevention.

The invention relates to the technical field of medical instruments, and provides a percutaneous reflux carotid artery blood supply reconstruction system which comprises an artery sheath, a filter, a pipeline and a vein sheath. The artery sheath, the filter and the vein sheath are sequentially connected through pipelines; the artery sheath comprises an artery sheath tube, a first balloon, a needle seat and a second balloon, and the artery sheath tube is coated with the first balloon; the first end of the needle seat is connected with the artery sheathing canal, the second end of the needle seat is coated with the second balloon and communicated with the second end of the needle seat, and the third end of the needle seat is connected with the pipeline; a first channel and a second channel are arranged in the artery sheath tube, the first channel is connected with the pipeline, and the two ends of the second channel are communicated with the first balloon and the second end of the needle seat respectively. According to the percutaneous reflux carotid artery blood supply reconstruction system, trauma to blood vessels is reduced, the risk of stroke caused by placement of a far-end protective umbrella is avoided, and the safety of an operation is improved; cerebral apoplexy caused by the fact that emboli enter the brain is avoided; and the blocking effect of the carotid artery is ensured.

Disclosed is the compound of Formula (I), and pharmaceutical salts thereof, for use in the treatment of stroke (e.g. acute ischaemic stroke and haemorrhagic stroke) and post-stroke pain, wherein the compound is administered to a subject less than 6 hours from stroke onset. Also disclosed is the compound for use in preventing or reducing the risk of iatrogenic stroke and pharmaceutical compositions comprising the compound.

The present application relates to the technical field of medical prediction, in particular to a web-based big data-driven stroke risk prediction method, comprising the following steps: S1, multi-scale data acquisition and alignment: physiological data, behavior data and health status data of a user are collected; S2, causal path identification: a causal feature subgraph with a confidence rating is generated; S3, feature fusion modeling: fluctuation features in real-time physiological data are modeled in response, trend features are extracted from behavior data, and fusion weights of fluctuation features and trend features are dynamically allocated to generate unified risk representation features; S4, risk evolution simulation: the influence of different intervention strategies on stroke risk is simulated to generate a risk evolution trajectory; S5, intervention scheme generation: a personalized intervention scheme is generated. The present application not only improves the accuracy of risk prediction, but also can respond to changes in the patient's health status in real time.

The invention relates to the technical field of stroke monitoring, and discloses a method and a system for detecting the stroke risk after waking of a home elder based on water and electricity information, and the method comprises the steps: synchronously obtaining household water, electricity and body data in a morning waking monitoring time period, and generating a morning waking water event sequence and an electricity load sequence through flow sequence form segmentation and electricity event steady-state analysis; morning awakening physiological indexes are formed according to the skin electricity, the heart rate and other data; determining a unified time reference according to a morning beginning behavior completion moment or a physiological composite sign, constructing an alignment time window, and mapping the three types of data to a discrete time grid; establishing double-hypothesis probability inference, respectively constructing water utilization, power utilization and body three-channel observation likelihoods, and performing joint fusion with a dependency relationship to obtain a current occurrence probability; and establishing a survival analysis model by using the time variation covariable in the aligned time window, and giving the occurrence probability of the future time period. The method does not depend on videos, and the judgment capability can be kept when the body data are missing.

The present disclosure relates to a method and system for acquiring and analyzing physiological signals of a subject to detect the risk or occurrence of stroke or cerebral infarction. A wearable wireless device may be used with one or more electrode clusters to record physiological signals during a sleep-wake cycle. The physiological signal may be utilized to perform sleep and wakefulness analysis. In some examples, physiological signals corresponding to each hemisphere of the brain may be compared, e.g., using coherence, to quantify asymmetry across the hemispheres and identify possible stroke, asymptomatic cerebral infarction (SBI), or aneurysm. Biomarkers may be extracted based on sleep and wakefulness analysis as well as coherence. Biomarkers may include an indication of obstructive sleep apnea or apnea risk, a reduction in slow wave sleep duration, or the presence of SBI or aneurysm. A stroke risk score may be generated from the biomarkers using a predictive model for the early detection of individuals that are potentially at risk of stroke.

Provided are methods of determining risk for chronic stress and stroke. More specifically, provided is an early prognostic index that can be used to predict chronic stress and stroke risk. There is provided a method of evaluating the risk of developing chronic stress and stroke, the method including obtaining a biological sample from an individual; measuring the levels of a set of biomarkers in the biological sample obtained from the individual; measuring the levels of a set of clinical markers of the individual; using a computer to programmatically generate an index based on the levels of biomarker in the biological sample obtained from the individual in combination with levels of the individual's clinical marker; and using the index to identify a likelihood that the individual will experience chronic stress and stroke.

The invention provides a covered stent, a covered stent delivery system and an intervention system, the covered stent comprises: a main covered stent having at least one pre-opening; and at least part of the guide wire is located at the pre-opened hole, and the guide wire is in releasable connection with the main covered stent. The covered stent embedded with the guide wire can be assembled into a standardized module in advance before an operation, then the standardized module is pressed, held and loaded in a conveying system, in an interventional operation, the stent is conveyed to a target position of an aortic arch through a femoral artery approach, after the main covered stent is released, the conveying system is withdrawn, the guide wire is disengaged from a side hole of a middle tube and exposed, and the guide wire is released from the side hole of the middle tube. An operator can directly use the exposed guide wire to guide the rapid exchange catheter to enter a branch blood vessel to complete passage establishment without additionally puncturing a carotid artery or a brachial artery, so that the operation time is remarkably shortened, and the stroke risk is reduced.

This invention provides a method and system for predicting stroke risk, relating to the fields of medical and health information technology and artificial intelligence. By aggregating multimodal clinical features into a high-level pathophysiological feature group and utilizing graph neural networks for risk prediction, this invention effectively solves the problem of fragmented interpretability in traditional AI models. Furthermore, it introduces a medical knowledge graph to verify and label the contribution of the model output, ensuring that risk attribution aligns with medical common sense and pathological mechanisms. The resulting three-layer interpretability report not only enhances the clinical credibility and understandability of the prediction results but also directly outputs evidence-based intervention recommendations, thus truly realizing a closed loop from risk prediction to clinical decision support and powerfully promoting the practical application of AI-assisted diagnosis and treatment systems.

This invention discloses a method and system for pontine infarct segmentation and END prediction based on multimodal joint learning. The method includes acquiring and preprocessing multimodal data; constructing a wavelet transform-based feature encoding network to perform multi-scale decomposition and detail preservation of features; constructing a dual-task guided fusion module to align the deep semantics of clinical variables and imaging features and generate task-specific representations; constructing a Mamba-based global feature aggregation module to model sequence dependencies using a state-space model; constructing a multimodal second-order fusion classifier to enhance the clinical-image interaction modeling using second-order statistics; and employing a two-stage joint training strategy for training and prediction, and outputting the prediction results. This invention utilizes the DWT / IWT mechanism to significantly improve the accuracy of capturing small pontine infarct lesions; it achieves explicit interaction between segmentation evidence and prediction signals, significantly improving the segmentation accuracy of small lesions and the reliability of stroke risk assessment.

The invention relates to the field of smart home, and discloses a multi-module control system for an intelligent household pillow, which comprises a sensing acquisition module, a data processing module, an execution control module and a communication interaction module, the sensing acquisition module is used for acquiring physiological data, behavior data and environment data when a user sleeps; the data processing module is used for performing feature extraction, calculation and analysis on the collected data and generating a control instruction and a health assessment result; the execution control module is used for adjusting the pillow supporting structure according to the control instruction; and the communication interaction module is used for realizing data transmission between the modules and information interaction with an external terminal. The physiological data, the behavior data and the environment data are collected cooperatively through the multiple sensing units, effective snoring events, stroke risk abnormity and staying up late behaviors are accurately recognized in combination with the special analysis unit, an adjusting instruction or early warning information can be generated in time, and active protection is provided for sleep health of a user.