Join us in our six part monthly webinar series featuring family medicine leaders in Canada who will share their expertise in machine learning, natural language processing (teaching computers to understand human language), ethics, and AI research.
This one-credit-per hour Group Learning activity has been certified by CFPC for up to one Mainpro+® credit for each webinar session.
Title: Machine Learning Applied to Primary Care EMR Data for Classification
Details: Dr. Tyler Williamson is an Associate Professor of Biostatistics, Department of Community Health Sciences and Associate Director of the Centre for Health Informatics at the Cumming School of Medicine, University of Calgary. Dr. Williamson is internationally recognized as an expert in health data science and biostatistics and helped lay some of the methodological foundations for the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), Canada’s first and largest primary care electronic medical record surveillance system. Dr. Williamson serves on the NAPCRG Big Data Task Force, is the past Canadian Co-Char of Research Methods in the Committee in the Advancement of the Science of Family Medicine in NAPCREG, and was winner of the NAPCREG New Investigator Award in 2018.
Stephanie Garies has a PhD in Epidemiology from the Department of Community Health Sciences and is the Assistant Director for the Southern Alberta Primary Care Research Network (SAPCReN) in the Department of Family Medicine, University of Calgary. Her research is focused on methods for improving EMR data quality, including the linkages of EMR and administrative data sources. Matt Taylor is a CPCSSN Data Manager at the University of Alberta, where his focus is on the development of CPCSSN’s machine learning-based electronic medical record coding and cleaning tools.
In this webinar, Dr. Williamson, Dr. Garies, and Dr. Matt Taylor, Data Manager will be sharing their work around use of ML for case definition development and data cleaning algorithms, and discuss why it may be important for those in family practice to be aware of the use of ML applied to EMR data.
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