Artificial Intelligence and Wearable Sensors to enable Digital Clinical Trials

Abstract

The Scripps Research Digital Trials Center leads groundbreaking studies that address the world’s most pressing health concerns. We leverage wearable sensors to re-engineer the clinical trial experience around the participant, passively and continuously collecting individual data. We process this large amount of data and propose new algorithms for prediction of patient outcomes.  
In the field of infectious diseases, we proposed DETECT, an app-based, nationwide clinical study to determine if individualized tracking of changes in wearable signals can provide early diagnosis and self-monitoring for COVID-19. We recruited 40,000 individuals and proposed algorithms for real-time detection of COVID-19, and for monitoring long-COVID and vaccine reactogenicity using wearable sensors.
On cardiovascular health, we propose a post-hoc explainability framework for deep learning models, focusing on atrial fibrillation detection and prediction for individuals wearing a sensor patch. We also showed how to reconstruct the 12-lead electrocardiogram via fewer leads using deep neural networks, using data from 1.6 million patients.
We are exploring challenges in multi-modal AI towards individualized prediction of glycemic response to food, and monitoring maternal health with wearable sensors.

About the speaker 

Giorgio Quer is an Assistant Professor and Director of Artificial Intelligence at the Scripps Research Translational Institute, where he is leading the Data Science and Analytics team working in the All of Us Research Program (NIH) and the Digital Trials Center.
His research focuses on artificial intelligence and probabilistic modeling applied to heterogeneous data signals, in order to extract key information and make predictions on future occurrences based on past data. He is involved in several digital medicine initiatives within the Digital Trial Center. In the DETECT study, he is developing algorithms to predict COVID-19 and other viral infections from wearable sensor data. He is responsible for the collaboration with several industrial partners, studying changes in heart rate and sleep data monitored by commercial wearable devices, and with the Halıcıoğlu Data Science Institute at UC San Diego. He is interested in the detection and modeling of atrial fibrillation from single-lead ECG signals.
He received his Ph.D. degree in Information Engineering from the University of Padova, Italy, and he continued his studies as a Postdoctoral researcher with the Qualcomm Institute at UC San Diego. He is a Senior Member of the IEEE and a Distinguished Lecturer for the IEEE Communications society.