Abstract: Artificial intelligence (AI) has emerged as a powerful tool for solving complex health problems using data-driven approaches. AI for health is fueled by both the advancement in AI methods and the availability of data provided by electronic health records (EHR) and wearables. This talk will explore the potential to support precision medicine using wearables that enable unobtrusive monitoring of patients in their daily lives. To harness the full potential of wearables, it is crucial to develop machine learning (ML) models to extract reliable clinical information from noisy and incomplete sensor data. Moreover, these ML approaches need to scale effectively across a wide range of sample sizes, providing robust predictions even with limited data, while enhancing predictive power with large datasets. We will highlight three clinical studies that use Fitbit wristbands as wearable instruments. First, we have established a robust feature engineering and ML pipeline specifically tailored for wearable studies with limited sample sizes. This pipeline demonstrated its effectiveness in predicting post-operative complications in a prospective clinical trial of patients undergoing pancreatic surgery. Second, we have developed WearNet, an end-to- end deep learning model designed to detect mental health disorders using wearable data. WearNet has been trained and validated on a large public dataset comprising 8,996 participants, including 1,247 diagnosed with mental disorders. Finally, we have explored multi-task ML approaches to predict individualized responses to depression therapy based on wearable data collected in a randomized controlled trial (RCT). By the end of the talk, we will discuss the opportunities and directions in the interdisciplinary field of AI and wearables for health, showcasing the transformative impact they can have on healthcare outcomes.
Bio: Chenyang Lu is the Fullgraf Professor of Computer Science & Engineering and holds joint appointments as Professor of Anesthesiology and in Medicine at Washington University in St. Louis. He is the founding director of the AI for Health Institute (AIHealth), a multidisciplinary institute dedicated to driving AI innovation in health research. His research interests include AI for health, Internet of Things, real-time systems, and cyber-physical systems. In 2022, he was honored with the Outstanding Technical Achievement Award and Leadership Award from the IEEE Technical Community on Real-Time Systems (TCRTS). He has also been recognized by a Test of Time Award from ACM Conference on Embedded Networked Sensor Systems (SenSys), an Influential Paper Award from IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), and nine Best or Outstanding Paper Awards. He is Editor-in-Chief of ACM Transactions on Cyber-Physical Systems. He also served as Editor-in-Chief of ACM Transactions on Sensor Networks, Chair of TCRTS and chaired leading conferences on IoT, real-time systems, and cyber-physical systems. He is a Fellow of ACM and IEEE.
