DTC Seminar Series
Machine Learning in Digital Medicine
Director of Artificial Intelligence
Scripps Research Translational Institute
San Diego, CA
Thursday, August 15, 2019
401/402 Walter Library
IEEE Comm Soc Distinguished Lecture Series
NOTE: registration required.
Please register by emailing Sandy Jobes your names and affiliation at email@example.com so that we can plan for the conference room, snacks and beverage in advance.
Digitalize human beings using biosensors to track our complex physiologic system, process the large amount of data generated with artificial intelligence (AI) and change clinical practice towards individualized medicine: these are the goals of digital medicine. At Scripps, we are a team of computer scientists, engineers, and clinical researchers, in partnership with health industries, and we propose new solutions to analyze large longitudinal data using statistical learning and deep convolutional neural networks to address different cardiovascular health issues. One of the greatest contributors to premature mortality worldwide is hypertension. Lowering blood pressure (BP) by just a few mmHg can bring substantial clinical benefits, but it is hard to assess the “true” BP for an individual, since it fluctuates significantly. With a dataset of 16 million BP measurements, we unveil the BP patterns and provide insights on the clinical relevance of these changes. Another prevalent health issue is atrial fibrillation (AF), the most common sustained cardiac arrhythmia, associated with stroke, heart failure and coronary artery disease. AF detection from single-lead electrocardiography (ECG) recordings is still an open problem, as AF events may be episodic and the signal noisy. We conduct a thoughtful analysis of recent convolutional neural network architectures developed in the computer vision field, redesigned to be suitable for a one-dimensional signal, and we evaluate their performance in the detection of AF using 200 thousand seconds of ECG, highlighting the potential and pitfall of this technology.
Looking to the future, we investigate new applications for wearable devices and advanced processing in the All of Us Research Program, an unprecedented research effort to gather data from one million people in the USA to accelerate the advent of precision medicine.
Parking and Directions:
Please contact Sandy_Jobes@starkey.com for a parking voucher if you are from outside University of Minnesota and need to park.
Giorgio Quer joined Scripps Research Translational Institute in January 2017. His expertise is in 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. His contributions include new methods exploiting compressive sensing to collect and process wireless sensor network data, data link layers protocols for cognitive networks, and new ways to extract information from heart rate variability in order to study group dynamics.
Quer’s research interests are focused on the interpretation and representation of big data for human health, in order to build models for prediction of future occurrences and improve patient outcomes. His multi-disciplinary interests include theoretical models, such as compressive sensing, Bayesian analysis, wavelet coherence and Markov decision processes; and analysis of noisy time-series from wearables, in particular physiological signals such as blood pressure, heart rate variability and photoplethysmography.
At the Translational Institute he works on the data analytic side of the All of Us Research Program, adopting probabilistic models and predictive analytics to extract information from large health datasets available through the program, as well as from other industrial collaborations. His goal is to extract and present this information in a useful way to clinicians and other users.
Quer received his undergraduate and master's degrees with honors in Telecommunications Engineering, and a doctorate in Information Engineering from the University of Padova, Italy. During his doctoral studies, he was a visiting researcher at the Centre for Wireless Communication at the University of Oulu, Finland, and at the California Institute for Telecommunications and Information Technology at the University of California, San Diego. Prior to joining the Translational Institute, he was a postdoctoral researcher at the Qualcomm Institute, University of California, San Diego. He currently serves as a reviewer for several IEEE and ACM journals, and he was the co-chair for the CQRM symposium at IEEE GLOBECOM 2015.
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