Cyber-Physical Systems Laboratory
Department of Computer Science and
Engineering
McKelvey School of Engineering
Washington University
Internet of Medical Things (IoMT)
provides a new clinical tool for digital phenotyping and outcome prediction of
patients in clinical and community settings. IoMT is driven by the rapid growth
of wearable devices and machine learning algorithms. The talk will report our
recent studies to monitor outpatients using wearables and develop machine
learning models to predict clinical deterioration. As a case study I will
present a pilot study to predict readmissions of congestive heart failure
patients recently discharged from Barnes-Jewish Hospital. The results
demonstrated the feasibility of continuously monitoring outpatients using
wristbands. We observed high levels of patient compliance in wearing the
wristbands regularly and satisfactory yield of data collection from the
wristbands to a cloud-based database. Machine learning models based on
multimodal data (step, sleep and heart rate) significantly outperformed the
traditional clinical approach based on LACE index. We will discuss the best of
practices in applying machine learning to small and unbalanced dataset common
in wearable-based clinical studies. We will also provide an overview of our
ongoing effort to predict surgical complications with Fitbit and our initial
experience employing smart watches for mental health and mobility assessment.
Chenyang Lu is
the Fullgraf
Professor in
the Department of Computer Science and Engineering at Washington University in
St. Louis. His research interests include embedded and real-time systems,
cyber-physical systems, Internet of Things, mobile health, and clinical AI. The
author and co-author of over 200 research papers with over 24,000
citations and an h-index of 72, Professor Lu is Editor-in-Chief
of ACM Transactions on Cyber-Physical Systems. He also served as
Editor-in-Chief of ACM Transactions on Sensor Networks from 2011 to 2017 and
Chair of the IEEE Technical Committee on Real-Time
Systems (TCRTS) from
2018 to 2019. He received the Ph.D. degree from University of Virginia in 2001.
He is a Fellow of ACM and IEEE.