Date: Thursday, November 20, 2025
Time: 1:00–2:00 p.m. Eastern time
Location: On Zoom
Join us for “Deep Learning with ECG Data in the ICU: From Modelling to Actionable AI” at the next NISS-CANSSI Collaborative Data Science Webinar.
This session will examine how deep learning methods can be leveraged to analyze electrocardiogram (ECG) data collected in intensive care units (ICUs), where rapid, reliable interpretation of patient information is crucial. The discussion will span the full pipeline—from methodological advances in modelling ECG signals to the translation of AI-driven insights into tools that can support real-time decision-making at the bedside. Together, the speakers will bridge perspectives from computer science and clinical practice, offering insights into both the technical challenges of modelling high-dimensional physiological time-series data and the practical considerations required to make AI trustworthy, interpretable, and actionable in critical care environments.
This webinar will feature David Maslove (Queen’s University and Kingston Health Sciences Centre) and Parvin Mousavi (Queen’s University). It will be moderated by Joel Dubin (University of Waterloo).
David Maslove is a Clinician Scientist in the Departments of Medicine and Critical Care Medicine at Queen’s University, and an Internist and Intensivist at Kingston Health Sciences Centre. His research focuses on the use of physiologic and genomic data to advance precision medicine in the ICU. Dr. Maslove completed medical school and residency in Internal Medicine at the University of Toronto. He trained in Critical Care Medicine at Stanford University where he also completed graduate studies in Biomedical Informatics. He is a member of the Canadian Critical Care Trials Group, and the Society of Critical Care Medicine, and since 2018 has been the Associate Editor for Data Science for Critical Care Medicine. See profile
Parvin Mousavi is the Director of the School of Computing at Queen’s University. Her research interests are in computer-aided diagnosis and interventions. These include:
Joel Dubin is a leading methodological statistician whose work focuses on longitudinal data analysis, especially multivariate and time-varying outcomes. He develops tools for modelling multiple physiological measurements over time—such as heart rate, respiratory rate, or blood pressure—using advanced techniques like curve-based methods, derivatives, and lagged effects. He also works on change-point and latent response models, prediction models that leverage subject similarity, and methods to handle missingness and complexity in real-world health data. His research spans a range of applications including intensive care, electronic health records, mobile health, child and aging populations, nephrology, cancer, nutrition, smoking cessation, and environmental health. Dr. Dubin received his MS in Applied Statistics from Villanova University, then worked in health services research at the U.S. Veterans Affairs and the MD Anderson Cancer Center. He earned his PhD in Statistics from the University of California-Davis, followed by a faculty appointment at Yale University. In 2005 he joined the University of Waterloo with a joint appointment in Statistics & Actuarial Science and Health Studies & Gerontology (now the School of Public Health Sciences). See profile.
In an era where data transcends traditional boundaries, fostering interdisciplinary collaboration has never been more crucial. Together with the National Institute of Statistical Sciences (NISS), we are proud to present the NISS-CANSSI Collaborative Data Science webinar series dedicated to showcasing data scientists and domain scientists from diverse scientific fields who collaborate to advance science. This initiative celebrates the power of collaboration, demonstrating how the fusion of data science with various disciplines can drive innovation, solve complex problems, and push the frontiers of knowledge beyond the realm of statistics.
Each session features two speakers: a data scientist and a subject matter expert from another domain who have successfully partnered to achieve impactful results. Through their shared experiences and insights, attendees gain a deeper understanding of the collaborative processes that bridge gaps between different scientific landscapes. These seminars not only highlight successful partnerships but also provide a platform for exchanging ideas, methodologies, and best practices that inspire new collaborations.