A celebration of six teams of interdisciplinary researchers and their new and emerging projects in data science and artificial intelligence

The Dalla Lana School of Public Health’s Data Science Interdisciplinary Research Cluster brings together an interdisciplinary team to focus on the DLSPH priority area: “Using data sciences, artificial intelligence and emerging technologies in informatics and analytics to improve population health and health systems performance”.

Help us celebrate the six teams of interdisciplinary researchers who won seed funding from DLSPH to investigate new and emerging projects in data science and artificial intelligence.

FUNDED PROPOSALS – 2021-22 DATA SCIENCE SEED GRANTS

Using Decision Tree Machine Learning to Identify Worker Movement Typologies

Presenter: Aviroop Biswas

Dr. Avi Biswas is an assistant professor in Epidemiology at DLSPH and an associate scientist at the Institute for Work & Health. His research informs strategies to promote the overall health and wellbeing of workers in addition to their on-the-job safety. Relevant to this project, one of his main research interests involves using exploratory statistical methods and machine learning on device-measured movement data to explore the complex factors that affect working Canadians’ ability to participate in physical activity.

Presenter: Cynthia Chen

Cynthia Chen is a biostatistician at the Institute for Work & Health. Her research interests include the classification of patterns from data collected longitudinally, multivariate longitudinal analysis, the examination and the comparison of risk factors of workplace injury, and the examination of gender differences in workplace injuries and workplace activity limitations.

A Multicentre Database of Patients Hospitalized with Diabetes in Ontario Across 30 Hospitals

Presenter: Michael Colacci

Michael Colacci is a fellow in general internal medicine and a master’s student in clinical epidemiology at the Dalla Lana School of Public Health. His research focuses on the creation of an inpatient diabetes database for patients hospitalized in Ontario and the application of machine learning to improve the quality of patient care.

The Evolution of Potentially Inappropriate Prescribing in Persons with Dementia

Presenter: Abby Emdin

Abby Emdin is a PhD student in Epidemiology at the Dalla Lana School of Public Health, University of Toronto, completing the emphasis in Artificial Intelligence and Data Science. Her research interests include aging, dementia, and pharmacoepidemiology, and how research can leverage routinely collected data to better understand these topics. Her thesis work focuses on applying emerging data science methods to describe and optimize medication use in older adults living with dementia, using population-level health administrative data. Prior to starting her PhD, Abby completed a MSc. at McMaster University, and an MPH at DLSPH, and worked as a Data Analyst at SickKids.

Presenter: Susan E. Bronskill

Susan E. Bronskill, PhD is a Senior Scientist and Lead of the Life Stage Research Program at ICES. She is a Professor in the Institute of Health Policy, Management & Evaluation and the Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, an Associate Scientist at Sunnybrook Research Institute, and an Adjunct Scientist at Women’s College Research Institute. Dr. Bronskill is an expert in health services research with a focus on older adults, pharmacoepidemiology and neurodegenerative disease. In her research, she makes use of population-based administrative databases to study transitions between health care sectors and focusses on improving quality of care, medication use, health services utilization and health care outcomes — particularly in persons with Alzheimer’s and related dementias, women and persons living in long-term care.

Building Fair Machine Learning Models: Using Big Data to Explore Inequities in Risk Assessment at the Centre for Addiction and Mental Health

Presenter: Dr Laura Sikstrom

Laura Sikstrom is a medical anthropologist and Project Scientist at CAMH’s Krembil Centre for Neuroinformatics. Most of her current work uses ethnographic insights to study the messy, but ordinary, business of turning data into clinically relevant predictions and decision-making tools. She is also interested in developing innovative interdisciplinary approaches to predictive modelling that advance health equity.

Presenter: Dr Marta Maslej

Marta Maslej is a CIHR-funded postdoctoral trainee at CAMH’s Krembil Centre for Neuroinformatics. Her research involves using methods from natural language processing and machine learning to predict outcomes in depression, with a focus on psychosocial and cognitive factors. She is also interested in the impact of predictive modelling on clinical decision-making and patient care.

ExplAIn 2 Kids: Engaging Children & Youth in Ar@ficial Intelligence in Pediatric Healthcare

Presenter: Melissa McCradden

Dr. McCradden is a Bioethicist with the Department of Bioethics and Researcher with the Genetics & Genome Biology Program at The Hospital for Sick Children. Melissa holds a PhD in Neuroscience (McMaster University) and a M.HSc. in Bioethics (University of Toronto). She is an Assistant Professor at the University of Toronto. In her role, she provides clinical and organizational consultation, gives education to staff and trainees, participates and leads policy development, and conducts research. Her areas of scholarship and research include artificial intelligence/machine learning, precision child health, paediatric bioethics, and research ethics. She is a member of the AI in Medicine Steering Committee at SickKids and sits on multiple international consensus groups pertaining to robust clinical evaluation of medical AI systems.

Presenter: Kelly Thai

Kelly Thai, MPH, is a research assistant at The Hospital for Sick Children.

A Predictive Model for the Presence of Occult Cancer in Family Practice Patients

Presenter: Vasily Giannakeas

Vasily Giannakeas is a PhD candidate in epidemiology at the Dalla Lana School of Public Health. Vasily has a background in engineering and epidemiology. His research interests are in the application of administrative health data to answer questions related to cancer.