Portrait of David Buckeridge

David Buckeridge

Associate Academic Member
Full Professor, McGill University, Department of Epidemiology, Biostatistics and Occupational Health
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Biography

David Buckeridge is a professor at the School of Population and Global Health at McGill University, as well as chief digital health officer for the McGill University Health Centre and executive scientific director of the Public Health Agency of Canada.

A Tier 1 Canada Research Chair in Health Informatics and Data Science, Buckeridge has projected health system demand for the Canadian province of Quebec, led data management and analytics for the Canadian Immunity Task Force, and supported the World Health Organization in monitoring global immunity to SARS-CoV-2. He has an MD from Queen's University, an MSc in epidemiology from the University of Toronto and a PhD in biomedical informatics from Stanford University. He is a Fellow of the Royal College of Physicians of Canada.

Current Students

Eva Gao
PhD - McGill University
eva.gao@mila.quebec
Gabriel Potvin
Master's Research - McGill University
gabriel.potvin@mila.quebec
Kalash Dave
Master's Research - McGill University
dave.kalash@mila.quebec
Rada Rusu
Master's Research - McGill University
rada.rusu@mila.quebec
Yannan Shen
Master's Research - McGill University
yannan.shen@mila.quebec

Publications

Revisiting Transfer Functions: Learning About a Lagged Exposure-Outcome Association in Time-Series Data
Hiroshi Mamiya
Alexandra M. Schmidt
Erica E. M. Moodie
David Buckeridge
2022-07-11
International Journal of Public Health (published)
doi.org
Survival Modelling for Data From Combined Cohorts: Opening the Door to Meta Survival Analyses and Survival Analysis Using Electronic Health Records
James H. McVittie
Ana F. Best
David B. Wolfson
David A. Stephens
Julian Wolfson
David Buckeridge
Shahinaz M. Gadalla
Non‐parametric estimation of the survival function using observed failure time data depends on the underlying data generating mechanism, i… (see more)ncluding the ways in which the data may be censored and/or truncated. For data arising from a single source or collected from a single cohort, a wide range of estimators have been proposed and compared in the literature. Often, however, it may be possible, and indeed advantageous, to combine and then analyse survival data that have been collected under different study designs. We review non‐parametric survival analysis for data obtained by combining the most common types of cohort. We have two main goals: (i) to clarify the differences in the model assumptions and (ii) to provide a single lens through which some of the proposed estimators may be viewed. Our discussion is relevant to the meta‐analysis of survival data obtained from different types of study, and to the modern era of electronic health records.
2022-06-16
International Statistical Review (published)
doi.org
Modeling electronic health record data using a knowledge-graph-embedded topic model
Yuesong Zou
Ahmad Pesaranghader
Aman Verma
David Buckeridge
Yue Li
The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic wa… (see more)y. However, effective extraction of clinical knowledge from the EHR data has been hindered by its sparsity and noisy information. We present KG-ETM, an end-to-end knowledge graph-based multimodal embedded topic model. KG-ETM distills latent disease topics from EHR data by learning the embedding from the medical knowledge graphs. We applied KG-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on EHR reconstruction and drug imputation. KG-ETM demonstrated superior performance over the alternative methods on both tasks. Moreover, our model learned clinically meaningful graph-informed embedding of the EHR codes. In additional, our model is also able to discover interpretable and accurate patient representations for patient stratification and drug recommendations.
2022-06-03
ArXiv (preprint)
doi.org
arxiv.org
A Conceptual Framework for Representing Events Under Public Health Surveillance
Anya Okhmatovskaia
Yannan Shen
Iris Ganser
Nigel Collier
Nicholas B King
Zaiqiao Meng
David Buckeridge
Information integration across multiple event-based surveillance (EBS) systems has been shown to improve global disease surveillance in expe… (see more)rimental settings. In practice, however, integration does not occur due to the lack of a common conceptual framework for encoding data within EBS systems. We aim to address this gap by proposing a candidate conceptual framework for representing events and related concepts in the domain of public health surveillance.
2022-05-25
Medical Informatics Europe (published)
doi.org
Mortality trends and length of stays among hospitalized patients with COVID-19 in Ontario and Québec (Canada): a population-based cohort study of the first three epidemic waves
Yiqing Xia
Huiting Ma
David Buckeridge
M. Brisson
Beate H Sander
A. Chan
Aman Verma
Iris Ganser
Nadine Kronfli
Sharmistha Mishra
Mathieu Maheu-Giroux
2022-04-01
International Journal of Infectious Diseases (published)
doi.org
Mortality trends and length of stays among hospitalized patients with COVID-19 in Ontario and Québec (Canada): a population-based cohort study of the first three epidemic waves
Yiqing Xia
Huiting Ma
David Buckeridge
Marc Brisson
Beate Sander
Adrienne Chan
Aman Verma
Iris Ganser
Nadine Kronfli
Sharmistha Mishra
Mathieu Maheu-Giroux
2022-04-01
International Journal of Infectious Diseases (published)
doi.org
Stringency of containment and closures on the growth of SARS-CoV-2 in Canada prior to accelerated vaccine roll-out
David Vickers
Stefan Baral
Sharmistha Mishra
Jeffrey C. Kwong
Maria Sundaram
Alan Katz
Andrew Calzavara
Mathieu Maheu-Giroux
David Buckeridge
Tyler Williamson
2022-02-23
International Journal of Infectious Diseases (published)
doi.org
Timeliness of reporting of SARS-CoV-2 seroprevalence results and their utility for infectious disease surveillance
Claire Donnici
Natasha Ilincic
Christian Cao
Caseng Zhang
Gabriel Deveaux
David A. Clifton
David Buckeridge
Niklas Bobrovitz
Rahul K. Arora
2022-02-18
Epidemics (published)
doi.org
Novel informatics approaches to COVID-19 Research: From methods to applications
Hua Xu
David Buckeridge
Fei Wang
P. Tarczy-Hornoch
2022-02-16
Journal of Biomedical Informatics (published)
doi.org
Geographic concentration of SARS-CoV-2 cases by social determinants of health in metropolitan areas in Canada: a cross-sectional study
Yiqing Xia
Huiting Ma
Gary Moloney
Héctor A. Velásquez García
Monica Sirski
Naveed Janjua
David Vickers
Tyler Williamson
Alan Katz
Kristy Yu
K. Yiu
Rafal Kustra
David Buckeridge
Marc Brisson
Stefan Baral
Sharmistha Mishra
Mathieu Maheu-Giroux
2022-02-14
CMAJ : Canadian Medical Association Journal (published)
doi.org
Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19
Zhi Wen
Guido Powell
Imane Chafi
David Buckeridge
Y. K. Li
Yue Li
2022-02-01
Patterns (published)
doi.org
Exploring social inequalities in healthcare trajectories following diagnosis of diabetes: a state sequence analysis of linked survey and administrative data
Rachel Marie Mckay
Laurence Letarte
Alain Gillian Lucie David Manon Catherine Anaïs Benoit A Vanasse Bartlett Blais Buckeridge Choinière Hudon
Alain Vanasse
Gillian M. Bartlett
Lucie Blais
David Buckeridge
Manon. Choinière
Catherine. Hudon
Anaïs Lacasse
Benoit Lamarche
Alexandre Lebel
Amélie Quesnel-Vallée
Pasquale Roberge
Valérie Émond
Marie-Pascale Pomey
Mike Benigeri
Anne-Marie Cloutier
Marc Dorais
Josiane Courteau … (see 15 more)
Mireille Courteau
Stéphanie Plante
Pierre Cambon
Annie Giguère
Isabelle Nogueira Leroux
Danielle St-Laurent
Denis Roy
Jaime Borja
A. Néron
Geneviève Landry
J. Éthier
Roxanne Dault
Marc-Antoine Côté-Marcil
Pier Tremblay
Sonia Quirion
2022-01-31
BMC Health Services Research (published)
doi.org