Internet of Medical Things

Predicting Clinical Outcomes and Digital Phenotyping with Wearables and Machine Learning


Chenyang Lu

Department of Computer Science & 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 initial experience employing smart watches for mental health and mobility assessment.


Video      Slides


Bio: 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 21,000 citations and an h-index of 67.  Professor Lu served as Editor-in-Chief of ACM Transactions on Sensor Networks from 2011 to 2017 and currently chairs the IEEE Technical Committee on Real-Time Systems (TCRTS).  He is a Fellow of IEEE.