AiCare Publications

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

Published March 1st, 2024

Authors: Phillip C Yang, MD; Alokkumar Jha, PhD; William Xu, BSc; Zitao Song, MSc; Patrick Jamp, BSc; Jeffrey J Teuteberg, MD

  • 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%


Precise Measurement of Physical Activities and High-Impact Motion: Feasibility of Smart Activity Sensor System

Published August 10th, 2020

Authors: Hung-Ping Liu, Yu-Min Chuang, Chih-Hao Liu, Phillip C. Yang, and Chiou-Shann Fuh, Member, IEEE

  • 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