Patient Health Monitoring using AI

Determining allowable operation ranges for controlling medical devices like surgical tools using machine learning to improve patient safety. The system analyzes historical data to train ML models that determine optimal input ranges for devices during procedures. When an operator tries to exceed these ranges, the system alerts them and blocks the input to prevent errors. The ML models are adaptive and can learn from feedback to improve the ranges over time.

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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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Predicting, detecting, and mitigating the effects of chronic health conditions using sensor data from wearable devices. The system analyzes sensor data to build baseline models that predict chronic condition symptoms based on wearable device data. It then deploys and refines individual user-specific models using active learning techniques. The models are used to predict chronic condition symptoms, which are logged and used to personalize treatment plans. The system also provides interventions based on predicted severe symptoms.

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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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Real-time abnormality detection and notification during medical imaging scans to help diagnose patients faster and more accurately. The system uses AI to analyze images in real-time as they are acquired and detect potential abnormalities. It then presents the user with a notification containing details of the potential abnormality and options to accept, postpone, or reject the notification. If accepted, a report is immediately generated. If postponed, the report is delayed until after the scan. If rejected, the abnormality is logged but not reported. This allows the user to focus on scanning while potentially important findings are flagged without interrupting the flow.

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Assistive robot system that uses sensors to monitor health characteristics of a person under care and make decisions based on that data. The system has an analysis module that receives health data from sensors, identifies the person's status using machine learning, and provides instructions to the robot's actuators to interact with the environment. It can also communicate with other systems using outputs from the robot. The robot can diagnose conditions, move around, and interact with the environment based on the person's health data.

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System for using diagnostics to provide personalized health guidance by leveraging machine learning models trained on labeled training data. The system receives user contextual information, generates a diagnostic output using a trained machine learning model, and transmits the diagnostic output to an advisor device. The diagnostic output classifies the user's physiological state based on contextual data. This is further linked to prognostic and ameliorative labels through additional training. The advisor can then use this diagnostic output to provide personalized guidance. The machine learning models are trained on labeled training data to accurately classify physiological states based on contextual inputs.

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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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Using machine learning models trained on media and medical data to accurately, consistently, and objectively detect, assess, quantify, and report pain levels experienced by patients. The models are trained on diverse datasets like emergency medical calls and image classifiers to identify pain presence and intensity. Pain levels can be predicted and verified using inputs like age, vital signs, and images. This allows objective pain quantification beyond subjective reports.

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Remote physical therapy system that uses AI and motion analysis to enable remote delivery of personalized physical therapy routines to patients. The system involves a client device at the patient's location with a virtual avatar to guide exercises, and a server with AI motion analysis to analyze videos and provide customized instructions. The AI analyzes motion data to monitor compliance and progress. The system can also notify caregivers of issues.

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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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AI-assisted medical imaging and report generation system that integrates AI algorithms into a unified user interface to improve efficiency and accuracy of medical image interpretation and reporting. The system provides AI-assisted image segmentation and labeling, AI-assisted dictation of findings, bi-directional dynamic linking of findings, AI finding display and interaction, and AI-enabled quality metrics. It generates reports with AI-assisted findings that can be accepted by the user and automatically incorporated into the report. The system aims to provide a complete radiology workflow with integrated AI functionality.

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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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Automatically determining medical recommendations for patients based on multiple medical images from different modalities using a machine learning classifier. The system maps regions of interest from images of different modalities related to a patient's condition, generates annotations and clinical data for each ROI, inputs it into the classifier, and uses it to determine a medical recommendation for the patient's condition. This allows integrating and analyzing images from different modalities to provide more informed and comprehensive medical recommendations.

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