AiCare Publications

Cloud-Based Machine Learning Platform to Predict Clinical Outcomes at Home for Patients With Cardiovascular Conditions Discharged From Hospital: Clinical Trial

Hospitalizations account for almost one-third of the US $4.1 trillion health care cost in the United States and a substantial portion of these hospitalizations are attributed to readmissions
In addition to costing costing hospitals money, readmissions demoralize hospital physicians, nurses, and administrators
Given this high uncertainty of proper home recovery, intelligent monitoring is needed to predict the outcome of discharged patients to reduce readmissions
This study aims to develop a remote, low-cost, and cloud-based machine learning (ML) platform to enable the precision health monitoring of PA, or Physical Activity, which may fundamentally alter the delivery of home health care
Our platform consists of a wearable device, which includes an accelerometer and a Bluetooth sensor, and an iPhone connected to our cloud-based ML interface to analyze PA remotely and predict clinical outcomes
We enrolled 52 patients discharged from Stanford Hospital and achieved precise prediction of the patients’ clinical outcomes with a sensitivity of 87%, a specificity of 79%, and an accuracy of 85%

Over 72% of the world-wide population lives beyond the age of 65 and senior care has become one of the single most relevant challenges globally
In this study, we will examine the role of an advanced activity sensor platform consisting of to monitor the daily activity levels of seniors
This data will assist in understanding the lifestyle of the individual seniors to promote safety and improve the quality of life through measurement of physical strength and independence
The proposed system includes a wearable device for each senior, Internet of Thing (IoT) receiver environment, smart alert, and cloud-based machine learning algorithm with Application Processing Interface (API) enabled remote Internet access
In this study, we demonstrated the requirements to detect falls and assess physical activities for seniors
Our platform utilized Inertial Measurement Unit (IMU) sensor for high impact detection (fall detection) and physical activity level measurement