Portrait of David Buckeridge

David Buckeridge

Associate Academic Member
Full Professor, McGill University, Department of Epidemiology, Biostatistics and Occupational Health
Research Topics
Medical Machine Learning
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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

Aidan Aidan
Master's Research - McGill University
Github
Kalash Dave
Master's Research - McGill University
Eva Gao
PhD - McGill University
Gabriel Potvin
Master's Research - McGill University
Rada Rusu
Master's Research - McGill University
Yannan Shen
Master's Research - McGill University

Publications

MixEHR-Nest: Identifying Subphenotypes within Electronic Health Records through Hierarchical Guided-Topic Modeling
Ruohan Wang
Zilong Wang
Ziyang Song
David Buckeridge
Yue Li
Automatic subphenotyping from electronic health records (EHRs)provides numerous opportunities to understand diseases with unique subgroups a… (see more)nd enhance personalized medicine for patients. However, existing machine learning algorithms either focus on specific diseases for better interpretability or produce coarse-grained phenotype topics without considering nuanced disease patterns. In this study, we propose a guided topic model, MixEHR-Nest, to infer sub-phenotype topics from thousands of disease using multi-modal EHR data. Specifically, MixEHR-Nest detects multiple subtopics from each phenotype topic, whose prior is guided by the expert-curated phenotype concepts such as Phenotype Codes (PheCodes) or Clinical Classification Software (CCS) codes. We evaluated MixEHR-Nest on two EHR datasets: (1) the MIMIC-III dataset consisting of over 38 thousand patients from intensive care unit (ICU) from Beth Israel Deaconess Medical Center (BIDMC) in Boston, USA; (2) the healthcare administrative database PopHR, comprising 1.3 million patients from Montreal, Canada. Experimental results demonstrate that MixEHR-Nest can identify subphenotypes with distinct patterns within each phenotype, which are predictive for disease progression and severity. Consequently, MixEHR-Nest distinguishes between type 1 and type 2 diabetes by inferring subphenotypes using CCS codes, which do not differentiate these two subtype concepts. Additionally, MixEHR-Nest not only improved the prediction accuracy of short-term mortality of ICU patients and initial insulin treatment in diabetic patients but also revealed the contributions of subphenotypes. For longitudinal analysis, MixEHR-Nest identified subphenotypes of distinct age prevalence under the same phenotypes, such as asthma, leukemia, epilepsy, and depression. The MixEHR-Nest software is available at GitHub: https://github.com/li-lab-mcgill/MixEHR-Nest.
2024-10-17
ArXiv (preprint)
arxiv.org
arxiv.org
TrajGPT: Healthcare Time-Series Representation Learning for Trajectory Prediction
Ziyang Song
Qincheng Lu
Mike He Zhu
David Buckeridge
Yue Li
In many domains, such as healthcare, time-series data is irregularly sampled with varying intervals between observations. This creates chall… (see more)enges for classical time-series models that require equally spaced data. To address this, we propose a novel time-series Transformer called **Trajectory Generative Pre-trained Transformer (TrajGPT)**. It introduces a data-dependent decay mechanism that adaptively forgets irrelevant information based on clinical context. By interpreting TrajGPT as ordinary differential equations (ODEs), our approach captures continuous dynamics from sparse and irregular time-series data. Experimental results show that TrajGPT, with its time-specific inference approach, accurately predicts trajectories without requiring task-specific fine-tuning.
2024-10-10
NeurIPS.cc/2024/Workshop/TSALM (published)
openreview.net
openreview.net
TrajGPT: Healthcare Time-Series Representation Learning for Trajectory Prediction
Ziyang Song
Qincheng Lu
Mike He Zhu
David Buckeridge
Yue Li
In many domains, such as healthcare, time-series data is irregularly sampled with varying intervals between observations. This creates chall… (see more)enges for classical time-series models that require equally spaced data. To address this, we propose a novel time-series Transformer called **Trajectory Generative Pre-trained Transformer (TrajGPT)**. It introduces a data-dependent decay mechanism that adaptively forgets irrelevant information based on clinical context. By interpreting TrajGPT as ordinary differential equations (ODEs), our approach captures continuous dynamics from sparse and irregular time-series data. Experimental results show that TrajGPT, with its time-specific inference approach, accurately predicts trajectories without requiring task-specific fine-tuning.
2024-10-10
NeurIPS.cc/2024/Workshop/TSALM (published)
openreview.net
openreview.net
TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis
Ziyang Song
Qingcheng Lu
He Zhu
David Buckeridge
Yue Li
2024-10-03
ArXiv (preprint)
doi.org
arxiv.org
Correction: Economic evaluation of the effect of needle and syringe programs on skin, soft tissue, and vascular infections in people who inject drugs: a microsimulation modelling approach
Jihoon Lim
W Alton Russell
Mariam El-Sheikh
David Buckeridge
Dimitra Panagiotoglou
2024-09-09
Harm Reduction Journal (published)
doi.org
Development of a Framework for Establishing 'Gold Standard' Outbreak Data from Submitted SARS-CoV-2 Genome Samples
Yannan Shen
Russell Steele
David Buckeridge
Submitted genomic data for respiratory viruses reflect the emergence and spread of new variants. Although delays in submission limit the uti… (see more)lity of these data for prospective surveillance, they may be useful for evaluating other surveillance sources. However, few studies have investigated the use of these data for evaluating aberration detection in surveillance systems. Our study used a Bayesian online change point detection algorithm (BOCP) to detect increases in the number of submitted genome samples as a means of establishing 'gold standard' dates of outbreak onset in multiple countries. We compared models using different data transformations and parameter values. BOCP detected change points that were not sensitive to different parameter settings. We also found data transformations were essential prior to change point detection. Our study presents a framework for using global genomic submission data to develop 'gold standard' dates about the onset of outbreaks due to new viral variants.
2024-08-22
Medical Informatics Europe (published)
doi.org
Canada's Provincial Covid-19 Pandemic Modelling Efforts: A Review of Mathematical Models and Their Impacts on the Responses
Yiqing Xia
Jorge Luis Flores Anato
Caroline Colijin
Naveed Janjua
Michael Otterstatter
Mike Irvine
Tyler Williamson
Marie B. Varughese
Michael Li
Nathaniel Osgood
David J. D. Earn
Beate Sander
Lauren E. Cipriano
Kumar Murty
Fanyu Xiu
Arnaud Godin
David Buckeridge
Amy Hurford
Sharmistha Mishra
Mathieu Maheu-Giroux
2024-07-25
Canadian Journal of Public Health (published)
doi.org
Canada’s approach to SARS-CoV-2 sero-surveillance: Lessons learned for routine surveillance and future pandemics
Sheila F. O’Brien
Michael Asamoah-Boaheng
Brian Grunau
Mel Krajden
David Buckeridge
David M. Goldfarb
Maureen Anderson
Marc Germain
Patrick Brown
Derek R. Stein
Kami Kandola
Graham Tipples
Philip Awadalla
Amanda Lang
Lesley Behl
Tiffany Fitzpatrick
Steven J. Drews
2024-07-09
Canadian journal of public health (published)
doi.org
A Bayesian Non-Stationary Heteroskedastic Time Series Model for Multivariate Critical Care Data
Zayd Omar
David A. Stephens
Alexandra M. Schmidt
David Buckeridge
2024-07-02
Statistics in Medicine (published)
doi.org
arxiv.org
Economic evaluation of the effect of needle and syringe programs on skin, soft tissue, and vascular infections in people who inject drugs: a microsimulation modelling approach
Jihoon Lim
W Alton Russell
Mariam El-Sheikh
David Buckeridge
Dimitra Panagiotoglou
2024-06-28
Harm Reduction Journal (published)
doi.org
Temporal trends in disparities in COVID-19 seropositivity among Canadian blood donors
Yuan Yu
Matthew J Knight
Diana Gibson
Sheila F O’Brien
David Buckeridge
W Alton Russell
Abstract Background In Canada’s largest COVID-19 serological study, SARS-CoV-2 antibodies in blood donors have been monitored since 2020. … (see more)No study has analysed changes in the association between anti-N seropositivity (a marker of recent infection) and geographic and sociodemographic characteristics over the pandemic. Methods Using Bayesian multi-level models with spatial effects at the census division level, we analysed changes in correlates of SARS-CoV-2 anti-N seropositivity across three periods in which different variants predominated (pre-Delta, Delta and Omicron). We analysed disparities by geographic area, individual traits (age, sex, race) and neighbourhood factors (urbanicity, material deprivation and social deprivation). Data were from 420 319 blood donations across four regions (Ontario, British Columbia [BC], the Prairies and the Atlantic region) from December 2020 to November 2022. Results Seropositivity was higher for racialized minorities, males and individuals in more materially deprived neighbourhoods in the pre-Delta and Delta waves. These subgroup differences dissipated in the Omicron wave as large swaths of the population became infected. Across all waves, seropositivity was higher in younger individuals and those with lower neighbourhood social deprivation. Rural residents had high seropositivity in the Prairies, but not other regions. Compared to generalized linear models, multi-level models with spatial effects had better fit and lower error when predicting SARS-CoV-2 anti-N seropositivity by geographic region. Conclusions Correlates of recent COVID-19 infection have evolved over the pandemic. Many disparities lessened during the Omicron wave, but public health intervention may be warranted to address persistently higher burden among young people and those with less social deprivation.
2024-06-05
International Journal of Epidemiology (published)
doi.org
Machine Learning Informed Diagnosis for Congenital Heart Disease in Large Claims Data Source
Ariane Marelli
Chao Li
Aihua Liu
Hanh Nguyen
Harry Moroz
James M. Brophy
Liming Guo
David Buckeridge
Jian Tang
Archer Yang
Yue Li
2024-02-01
JACC: Advances (published)
doi.org