Seminars & Events

Tuesday, April 16, 2024, 15:00-16:00
The Health AI and Data Science (HAD) Program presents
Developing and Deploying Transparent and Reproducible Predictive Algorithms in Healthcare (Part 1 of 2)
Speaker: Doug Manuel and Wenshan Li
Dr. Doug Manuel holds the position of Senior Scientist at the Ottawa Hospital Research Institute and is a distinguished professor at uOttawa’s Department of Family Medicine and School of Epidemiology and Public Health, holding a Tier 1 Clinical Research Chair in Precision Medicine for Disease Prevention. Dr. Manuel holds an additional appointment as a senior investigator at Bruyère Research Institute.

Dr. Manuel’s research combines expertise in public health, healthcare systems, and primary care, focusing on understanding factors contributing to differences in population health outcomes across societies. This research involves developing and using advanced predictive algorithms and microsimulation models to assess the potential impact of health interventions and policy strategies. Dr. Manuel collaborates on Project Big Life, a website with disease risk calculators, helping millions globally to understand their health and contribute to evidence-based public health decisions.

Wenshan Li is a post-doctoral fellow at the Ottawa Hospital Research Institute, with a PhD in epidemiology. She is experienced in conducting health services research using large and/or complex data, and involved in several projects on predictive model development.
Location: Virtual via MS Teams. Meeting ID: 226 357 291 422 | Passcode: HyCFcM

Please contact Emma Brown at emmabrown1@ohri.ca if you would like the meeting link.

NOTE: If you would like to be added to the HAD - Health AI and Data Science team on MS Teams (including the HAD JC seminar mailing list), please join the team using code: owfh55e. If you are external to TOH/OHRI and would like to be added, please email Emma Brown at emmabrown1@ohri.ca.

This is a two-part seminar series. Part 2 of the seminar will be held in May by Juan Li and Kitty Chen from the Ottawa Hospital Research Institute.

Learning Objectives

By the end of the sessions, participants will:

  • Be able to apply open science principles in developing predictive algorithms to enhance reproducible science and improve patient care quality
  • Understand the approaches used by other participants, thereby improving researcher and IS collaboration for algorithm development and deployment

Part 1 – Foundations for reproducible predictive algorithms in healthcare

Objectives: review and discuss the essentials of reproducible and transparent AI, understanding its imperatives, best practices, and challenges in the healthcare context.

  • Review and discuss open science from the perspective of predictive algorithms in health care.
  • Examine case studies comparing open-source and proprietary workflows in algorithm development, discussing their implications for healthcare IT and patient care.

Part 2 – Practical application of open and reproducible predictive algorithms

Objectives: Engage in hands-on development and deployment of predictive algorithms using open-source tools, with a focus on real-world healthcare applications.

  • Review a hands-on example of algorithm development and deployment using an open-source workflow (R Tidymodels and Plumber).
  • Walk-through of algorithm development and deployment at the Project Big Life platform.
Tuesday, April 30, 2024, 15:00-16:00
The Health AI and Data Science (HAD) Program presents
Climbing the implementation mountain - Lessons in AI Deployment from Unity Health Toronto
Speaker: Amol Verma and Muhammad Mamdani
Amol Verma is a physician, scientist, and Assistant Professor in General Internal Medicine at St. Michael’s Hospital and the University of Toronto, Department of Medicine and the Temerty Professor of AI Research and Education in Medicine at the University of Toronto. He is a health services researcher, studying and improving hospital care using electronic clinical data. Amol co-founded and co-leads GEMINI, one of Canada’s largest hospital clinical data research networks and is a Provincial Clinical Lead for Quality Improvement in General Internal Medicine at Ontario Health.

Dr. Mamdani is Vice President of Data Science and Advanced Analytics at Unity Health Toronto and Director of the University of Toronto Temerty Faculty of Medicine Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM). Dr. Mamdani’s team bridges advanced analytics including machine learning with clinical and management decision making to improve patient outcomes and hospital efficiency. Dr. Mamdani is also Professor in the Department of Medicine of the Temerty Faculty of Medicine, the Leslie Dan Faculty of Pharmacy, and the Institute of Health Policy, Management and Evaluation of the Dalla Lana Faculty of Public Health. He is also adjunct Senior Scientist at the Institute for Clinical Evaluative Sciences (ICES) and a Faculty Affiliate of the Vector Institute. In 2010, Dr. Mamdani was named among Canada’s Top 40 under 40. He has published over 500 studies in peer-reviewed medical journals. Dr. Mamdani obtained a Doctor of Pharmacy degree (PharmD) from the University of Michigan (Ann Arbor) and completed a fellowship in pharmacoeconomics and outcomes research at the Detroit Medical Center. During his fellowship, Dr. Mamdani obtained a Master of Arts degree in Economics from Wayne State University in Detroit, Michigan with a concentration in econometric theory. He then completed a Master of Public Health degree from Harvard University with a concentration in quantitative methods.
Location: Virtual via MS Teams. Meeting ID: 239 664 003 632 | Passcode: 7GZbUE

Please contact Emma Brown at emmabrown1@ohri.ca if you would like the meeting link.

NOTE: If you would like to be added to the HAD - Health AI and Data Science team on MS Teams (including the HAD JC seminar mailing list), please join the team using code: owfh55e. If you are external to TOH/OHRI and would like to be added, please email Emma Brown at emmabrown1@ohri.ca.

Learning Objectives:

By the end of the seminar, participants will be able to:

  1. Understand an organizational approach to developing and deploying AI
  2. Identify key enablers of successful AI deployment
  3. Recognize the potential of AI-based early warning systems for patient deterioration
Tuesday, May 14, 2024, 15:00-16:00
The Health AI and Data Science (HAD) Program presents
TBD
Speaker: Gauruv Bose
Dr. Gauruv Bose is full-time academic neurologist at the Ottawa Hospital, Department of Medicine, Division of Neurology, and assistant professor at the University of Ottawa, Faculty of Medicine. He completed a Clinical Research Fellow in Multiple Sclerosis and Neuroimmunology at Mass General Brigham & Harvard Medical School, and is now primarily seeing patients with Multiple Sclerosis and related neuro-immunological disorders.

His research consists of investigating large real-world datasets of patients with Multiple Sclerosis (MS) to develop predictive models using machine learning techniques, as well as building on this research forefront by investigating the added value of implementing novel imaging and serum biomarkers into these predictive models.
Location: Virtual via MS Teams. Meeting ID: 267 677 792 865 | Passcode: aoyqYp

Please contact Emma Brown at emmabrown1@ohri.ca if you would like the meeting link.

NOTE: If you would like to be added to the HAD - Health AI and Data Science team on MS Teams (including the HAD JC seminar mailing list), please join the team using code: owfh55e. If you are external to TOH/OHRI and would like to be added, please email Emma Brown at emmabrown1@ohri.ca.
More details to come.
Tuesday, May 28, 2024, 15:00-16:00
The Health AI and Data Science (HAD) Program presents
Developing and Deploying Transparent and Reproducible Predictive Algorithms in Healthcare (Part 2 of 2)
Speaker: Juan Li and Kitty Chen
Juan Li is a Senior Clinical Research Associate in Neuroscience Program and Clinical Epidemiology Program at OHRI. Her main research interest includes predictive modelling, risk of Parkinson disease, machine learning, clinical research, and psychometrics.

Kitty Chen is a research assistant at the Ottawa Hospital Research Institute, with an MPH in epidemiology from the University of Toronto. She has been involved in several projects related to chronic diseases using big data and has a strong interest in open science.
Location: Virtual via MS Teams. Meeting ID: 256 113 661 42 | Passcode: dThgeo

Please contact Emma Brown at emmabrown1@ohri.ca if you would like the meeting link.

NOTE: If you would like to be added to the HAD - Health AI and Data Science team on MS Teams (including the HAD JC seminar mailing list), please join the team using code: owfh55e. If you are external to TOH/OHRI and would like to be added, please email Emma Brown at emmabrown1@ohri.ca.

This is a two-part seminar series. Part 1 of the seminar series will be held on April 16th by Doug Manuel and Wenshan Li from the Ottawa Hospital Research Institute. A recording of Part 1 will be available in the HAD JC MS Teams channel. If you would like to view the recording and do not have access, please email emmabrown1@ohri.ca to receive the link.

Learning Objectives:

By the end of the sessions, participants will:

  • Be able to apply open science principles in developing predictive algorithms to enhance reproducible science and improve patient care quality
  • Understand the approaches used by other participants, thereby improving researcher and IS collaboration for algorithm development and deployment

Part 1 – Foundations for reproducible predictive algorithms in healthcare

Objectives: review and discuss the essentials of reproducible and transparent AI, understanding its imperatives, best practices, and challenges in the healthcare context.

  • Review and discuss open science from the perspective of predictive algorithms in health care.
  • Examine case studies comparing open-source and proprietary workflows in algorithm development, discussing their implications for healthcare IT and patient care.

Part 2 – Practical application of open and reproducible predictive algorithms

Objectives: Engage in hands-on development and deployment of predictive algorithms using open-source tools, with a focus on real-world healthcare applications.

  • Review a hands-on example of algorithm development and deployment using an open-source workflow (R Tidymodels and Plumber).
  • Walk-through of algorithm development and deployment at the Project Big Life platform.
Tuesday, June 11, 2024, 15:00-16:00
The Health AI and Data Science (HAD) Program presents
TBD: Large Language Models Seminar Presentation (Part 1 of 2)
Speaker: Arya Rahgozar, Doug Manuel, and Juan Li
Arya Rahgozar, PhD is an adjunct professor of Data Science at Faculty of Engineering and postdoctoral fellow at the Department of Family Medicine, University of Ottawa. Arya did his master’s in management sciences, at the University of Waterloo. Arya''''''''s background is in applied mathematics and computer science with the focus on NLP and its applications in medicine.

Dr. Doug Manuel holds the position of Senior Scientist at the Ottawa Hospital Research Institute and is a distinguished professor at uOttawa’s Department of Family Medicine and School of Epidemiology and Public Health, holding a Tier 1 Clinical Research Chair in Precision Medicine for Disease Prevention. Dr. Manuel holds an additional appointment as a senior investigator at Bruyère Research Institute.

Dr. Manuel’s research combines expertise in public health, healthcare systems, and primary care, focusing on understanding factors contributing to differences in population health outcomes across societies. This research involves developing and using advanced predictive algorithms and microsimulation models to assess the potential impact of health interventions and policy strategies. Dr. Manuel collaborates on Project Big Life, a website with disease risk calculators, helping millions globally to understand their health and contribute to evidence-based public health decisions.

Juan Li is a Senior Clinical Research Associate in Neuroscience Program and Clinical Epidemiology Program at OHRI. Her main research interest includes predictive modelling, risk of Parkinson disease, machine learning, clinical research, and psychometrics.
Location: Virtual via MS Teams. Meeting ID: 221 062 051 071 | Passcode: TAHMTr

Please contact Emma Brown at emmabrown1@ohri.ca if you would like the meeting link.

NOTE: If you would like to be added to the HAD - Health AI and Data Science team on MS Teams (including the HAD JC seminar mailing list), please join the team using code: owfh55e. If you are external to TOH/OHRI and would like to be added, please email Emma Brown at emmabrown1@ohri.ca.
More details to come.
Tuesday, June 25, 2024, 15:00-16:00
The Health AI and Data Science (HAD) Program presents
TBD: Large Language Models Seminar Presentation (Part 2 of 2)
Speaker: Arya Rahgozar, Doug Manuel, and Juan Li
Arya Rahgozar, PhD is an adjunct professor of Data Science at Faculty of Engineering and postdoctoral fellow at the Department of Family Medicine, University of Ottawa. Arya did his master’s in management sciences, at the University of Waterloo. Arya''''''''s background is in applied mathematics and computer science with the focus on NLP and its applications in medicine.

Dr. Doug Manuel holds the position of Senior Scientist at the Ottawa Hospital Research Institute and is a distinguished professor at uOttawa’s Department of Family Medicine and School of Epidemiology and Public Health, holding a Tier 1 Clinical Research Chair in Precision Medicine for Disease Prevention. Dr. Manuel holds an additional appointment as a senior investigator at Bruyère Research Institute.

Dr. Manuel’s research combines expertise in public health, healthcare systems, and primary care, focusing on understanding factors contributing to differences in population health outcomes across societies. This research involves developing and using advanced predictive algorithms and microsimulation models to assess the potential impact of health interventions and policy strategies. Dr. Manuel collaborates on Project Big Life, a website with disease risk calculators, helping millions globally to understand their health and contribute to evidence-based public health decisions.

Juan Li is a Senior Clinical Research Associate in Neuroscience Program and Clinical Epidemiology Program at OHRI. Her main research interest includes predictive modelling, risk of Parkinson disease, machine learning, clinical research, and psychometrics.
Location: Virtual via MS Teams. Meeting ID: 298 473 539 616 | Passcode: iQ262y

Please contact Emma Brown at emmabrown1@ohri.ca if you would like the meeting link.

NOTE: If you would like to be added to the HAD - Health AI and Data Science team on MS Teams (including the HAD JC seminar mailing list), please join the team using code: owfh55e. If you are external to TOH/OHRI and would like to be added, please email Emma Brown at emmabrown1@ohri.ca.
More details to come.

Please note that OHRI seminars are open to all members of OHRI and partner institutions. Members of the general public are asked to contact the communications office (jganton@ohri.ca) for more information about the research presented at OHRI seminars.