Continuous Remote Monitoring
We propose creating a software that analyzes the data generated in real-time from devices used for continuous biological monitoring of vital signs and electrocardiogram (ECG). Our machine learning tool will serve the purpose of early detection of disease by detecting baseline abnormalities and identification of new features.
Continuous Monitoring
Using remote sensing devices, health data such as heart rate, blood pressure, and electrocardiogram tracings, are continuously collected.
Early Detection of Disease
Our machine learning tools are designed to detect critical abnormalities and identify new concerning features in real-time. This allows patients and physicians to detect disease processes earlier than ever before possible.
Real-time Analysis
Our machine learning tools are used to analyze patient data in real-time to detect critical abnormalities and abnormal changes based on the patient's baseline.
Clinical Decision Support System
Detected abnormalities are labeled, saved, and flagged for a physician to review. Based on the specific abnormality detected, a personalized recommendation will be automatically generated for the physician.
