Patient Vital Monitoring using AI

Non-invasive monitoring of blood analyte levels using electrocardiograms (ECGs) and machine learning. The technique involves training a machine learning model using ECGs and corresponding analyte levels from multiple individuals. The model can then predict analyte levels from new ECGs without needing blood samples. The training data is processed to filter out potentially inaccurate analyte measurements that could negatively impact model accuracy. This involves analyzing the ECGs to identify features that correlate with analyte levels, as some subtle changes in the ECG may indicate analyte fluctuations that are not obvious to the human eye. The model is trained using these identified ECG features rather than just the analyte measurements. By leveraging the relationship between ECGs and analyte levels, the model can accurately predict analyte concentrations without invasive blood draws.

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Health monitoring using multimodal sensors and external data to accurately diagnose diseases and track symptoms over time. The method involves continuously monitoring health using sensors like microphones, peak flow meters, pulse oximeters, etc. It also collects non-sensory data like medication, diet, location, etc. The sensory and non-sensory data is fed into neural networks to detect symptoms and diseases. The networks adapt periodically based on user data to improve accuracy and reduce false alarms. The method also generates personalized audio summaries for doctors to help diagnose.

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Wearable electronic device for monitoring health conditions of individuals like seniors with dementia or those at risk of urinary tract infections. The device has a hybrid wireless communication system that can selectively receive location data from indoor Bluetooth beacons and outdoor GPS. It transmits location over low-power wide area networks. This allows accurate indoor and outdoor location tracking. The device also sensors for environment, activity, and physiology. It analyzes the data using onboard machine learning to classify health conditions. Alerts are sent when the wearer leaves a zone. The device connects to a server for training the machine learning model.

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A wearable biosensing device that provides personalized health recommendations and risk assessments directly to patients. The device captures biosensing data like blood flow and oxygen levels using sensors mounted on the skin. This data is analyzed using machine learning models to predict health metrics like potassium levels. The predictions are then presented to patients via a graphical user interface on the device. This allows patients to monitor their health without blood tests and get personalized recommendations based on their biosensing data.

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Analyzing electrocardiogram (ECG) data using artificial intelligence to accurately and efficiently process ECG signals with enhanced accuracy. The system involves delineating the ECG waves, grouping beats together, and embedding the beats into a lower dimensional space for classification. This allows identifying hidden P waves, handling noisy signals, and handling multiple beats at once. The AI algorithms are trained on large datasets of ECGs from different patients.

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Smart mattress that monitors patient health, helps caregivers, and automatically adjusts patient positioning without human assistance. The mattress has sensors to collect biometric data, pressure sensors to measure patient weight distribution, actuators to deform the mattress, and a control system to coordinate motion. The mattress can detect vital signs, pressure ulcers, and posture issues. It can also identify falls and provide alerts. The mattress can autonomously adjust positioning at intervals to prevent bedsores. The mattress communicates with external devices like wearables and EMR systems.

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Multi-sensor health monitoring platform that provides personalized health monitoring using non-invasive, wearable, and point-of-care sensors in a patient's home to complement hospital and clinic testing. The platform leverages AI to predict patient needs, energy requirements, and sensor activation based on physiological and activity features. It schedules sensor recharging and identifies which sensors to activate. The platform collects sensor data, applies AI to generate insights and recommendations, and provides tailored care.

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A single neural network model for analyzing asynchronous bio-signals from multiple leads without requiring separate models for each lead combination. The model learns to derive an analysis value by reflecting the correlation between leads regardless of the specific combination acquired. This is done through encoding the lead position in the input features and using self-attention computations to propagate the correlation. The model can accurately predict cardiovascular disease even when not all leads are available, since it learns the overall lead interdependence.

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Automatic assessment of patient health using a cloud-connected, AI-powered health machine. The machine interacts with patients to collect initial data, determines risk factors, then asks further questions based on the risks. It processes the combined data using machine learning models to determine assessments, diagnoses, and treatment recommendations. The machine provides a comprehensive, point-of-care system for automated patient evaluation.

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A patient health management platform for managing metabolic diseases using continuously collected biosignals. The platform generates personalized treatments for metabolic diseases like diabetes by analyzing a unique combination of continuous biosignals from wearables, lab tests, nutrition, meds, and symptoms. It uses machine learning models to understand how biosignals impact metabolic health, generates time series of metabolic states, and recommends treatments based on the patient's profile.

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Cost-effective and accurate health monitoring system using wearable sensors and a low-cost processing unit. The system acquires multiple biomedical signals like ECG, PPG, motion, voice, and body temperature using a wearable sensor package. It processes these signals using machine learning models to monitor parameters like arrhythmia, heart rate, blood pressure, respiratory rate, and coughs. The models are trained on a conventional processor and then implemented on a low-cost processing unit. This allows real-time monitoring on commercial devices without needing expensive specialized hardware.

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Diagnosing health conditions using patient time series data like ECGs to improve accuracy and timeliness compared to relying solely on derived metrics. The method involves training neural networks on cohorts of patients with the condition versus controls to diagnose the condition based on the raw time series data. It preprocesses the time series by extracting metrics like QT interval, then aggregates network outputs for multiple metrics to generate a diagnosis. This leverages the full time series rather than just discrete metrics.

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Assistant robots that can observe signs of health issues, diagnose conditions, and provide medical assistance in homes, workplaces, and healthcare facilities. The robots use sensors to monitor vital signs, movements, and environments, and leverage machine learning models to interpret the data and determine if health dangers or distress is present. If so, the robots can take actions like alerting emergency services, administering first aid, and fetching medication. The robots can also provide regular health checks and treatments.

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Configuring input data for a neural network-based model to estimate physiological variables like blood pressure from non-invasively measured bio-signals like PPG. The input data is generated by combining bio-signal data from different times, allowing the model to learn variations in physiological variables over time. This trained model can then estimate current physiological values by inputting the combined historical data and a calibration value.

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Non-contact image-based blood pressure measurement using visual intelligence techniques to estimate blood pressure by analyzing video of a person's face. A neural network model is trained using webcam videos to extract features from pulse waves in the videos and predict blood pressure. The model can continuously estimate blood pressure without contact by analyzing videos from a webcam. It leverages visual intelligence to extract features directly from pulse waves instead of using cuff-based measurements.

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Predicting ventricular fibrillation (VF) onset in individuals at risk for sudden cardiac arrest using machine learning models trained on heart rate variability (HRV) features extracted from temporal beat activity. The models are trained on HRV from beat activity samples with and without VF events to learn patterns indicative of imminent VF. Inference involves applying the trained models to real-time beat activity to predict VF onset times.

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Unobtrusive monitoring system using ultra-wideband (UWB) radar to detect and classify patient motion patterns as indicative of undesirable events like falls, seizures, or sleep disturbances. The system learns normal behavior using machine learning on sensor data to adapt to individual patients. It generates alarms and insights based on deviations from normal patterns. The radar is placed in the patient's environment like a ceiling or nightstand. The system can also integrate other sensors like accelerometers, microphones, and vital sign monitors.

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Facilitating real-time estimation of physiological conditions like heart rate, blood pressure, and respiration rate during live video consultations using on-device AI. The system involves a client computer with a camera, storing a trained neural network to estimate physiological conditions from video, obtaining a live video stream during a session, providing video as input to the network to get condition estimates, and sharing those estimates with the remote client. This enables accurate, real-time monitoring and assessment of vital signs during telemedicine consultations using just the client's own camera.

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Determining vital signs and physiological states of patients using smart devices like smartphones with cameras, and initiating actions based on abnormal findings. The method involves capturing images of body parts using the device's camera, extracting vital signs like heart rate and oxygen saturation from the images using image processing, and using machine learning models to determine physiological states like hypertension or hypoxia. If the state is outside a threshold, it triggers actions like initiating a video call or making a phone call to a medical professional.

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Combining mobile cardiac telemetry (MCT) and Holter monitoring techniques to provide enhanced cardiac event analysis using machine learning. The method involves simultaneously analyzing ECG data for both real-time MCT monitoring and retrospective Holter monitoring. It uses a machine learning model to automatically detect cardiac events during MCT, determine event severity, and send notifications. For Holter, it uses the same model to classify event rhythms. By combining the studies, it provides more detailed analysis than MCT alone and retrospective rhythm classification like Holter.

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