Clinical Outcomes and Digital Phenotyping with Wearables and Machine Learning
Cyber-Physical Systems Laboratory
McKelvey School of Engineering
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
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
readmissions of congestive heart failure patients recently discharged
Barnes-Jewish Hospital. The results demonstrated the feasibility of
continuously monitoring outpatients using wristbands. We observed high
of patient compliance in wearing the wristbands regularly and
yield of data collection from the wristbands to a cloud-based database.
learning models based on multimodal data (step, sleep and heart rate)
significantly outperformed the traditional clinical approach based on
index. We will discuss the best of practices in applying machine
learning to small
and unbalanced dataset common in wearable-based clinical studies. We
provide an overview of our initial experience employing smart watches
mental health and mobility assessment.
Chenyang Lu is
the Fullgraf Professor in the McKelvey School of
Engineering at Washington University in St. Louis. His research interests
include Internet of Things, real-time and embedded systems, and cyber-physical
systems. Professor Lu's current work focuses on Internet of Medical Things that
combines wearable devices and clinical AI for predicting clinical outcomes and
digital phenotyping. The author and co-author of over
170 research papers with over 22,000 citations and an
h-index of 68.
Professor Lu served as Editor-in-Chief of ACM Transactions on Sensor Networks
from 2011 to 2017 and Chair of IEEE Technical Committee on
Real-Time Systems (TCRTS) from 2018 to 2019. He is a Fellow of IEEE.
- D. Li, J.
Vaidya, M. Wang, B. Bush, C. Lu, M. Kollef and T. Bailey, Feasibility Study of
Monitoring Deterioration of Outpatients Using Multi-modal Data Collected by Wearables, ACM Transactions
on Computing for Healthcare, 1(1), Article 5, March 2020.
- Healthy Mind Lab,
Department of Psychiatry, School of Medicine, Washington University,
- Institute for
Informatics, Research-in-Progress Lecture Series, School of Medicine, Washington
- Institute of Clinical and
Translational Sciences, Washington University, 10/23/2019.