Seminars & Events

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 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

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 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.

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 ( for more information about the research presented at OHRI seminars.