David Buckeridge

Nigel Collier

2024-01-01

AAAI (published)

Bidirectional Generative Pre-training for Improving Time Series Representation Learning

Ziyang Song

Qincheng Lu

Mike He Zhu

Yue Li

2024-01-01

arXiv.org (preprint)

CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data

Louis Mullie

Jonathan Afilalo

Patrick Archambault

Rima Bouchakri

Kip Brown

Yiorgos Alexandros Cavayas

Alexis F Turgeon

Denis Martineau

François Lamontagne

Martine Lebrasseur

Renald Lemieux

Jeffrey Li

Michaël Sauthier

Pascal St-Onge

An Tang

William Witteman

Michael Chassé

2023-12-21

J. Am. Medical Informatics Assoc. (published)

CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data

Louis Mullie

Jonathan Afilalo

Patrick Archambault

Rima Bouchakri

Kip Brown

Yiorgos Alexandros Cavayas

Alexis F Turgeon

Denis Martineau

François Lamontagne

Martine Lebrasseur

Renald Lemieux

Jeffrey Li

Michaël Sauthier

Pascal St-Onge

An Tang

William Witteman

Michael Chassé

Abstract Objectives Distributed computations facilitate multi-institutional data analysis while avoiding the costs and complexity of data po… (see more)oling. Existing approaches lack crucial features, such as built-in medical standards and terminologies, no-code data visualizations, explicit disclosure control mechanisms, and support for basic statistical computations, in addition to gradient-based optimization capabilities. Materials and methods We describe the development of the Collaborative Data Analysis (CODA) platform, and the design choices undertaken to address the key needs identified during our survey of stakeholders. We use a public dataset (MIMIC-IV) to demonstrate end-to-end multi-modal FL using CODA. We assessed the technical feasibility of deploying the CODA platform at 9 hospitals in Canada, describe implementation challenges, and evaluate its scalability on large patient populations. Results The CODA platform was designed, developed, and deployed between January 2020 and January 2023. Software code, documentation, and technical documents were released under an open-source license. Multi-modal federated averaging is illustrated using the MIMIC-IV and MIMIC-CXR datasets. To date, 8 out of the 9 participating sites have successfully deployed the platform, with a total enrolment of >1M patients. Mapping data from legacy systems to FHIR was the biggest barrier to implementation. Discussion and conclusion The CODA platform was developed and successfully deployed in a public healthcare setting in Canada, with heterogeneous information technology systems and capabilities. Ongoing efforts will use the platform to develop and prospectively validate models for risk assessment, proactive monitoring, and resource usage. Further work will also make tools available to facilitate migration from legacy formats to FHIR and DICOM.

2023-12-21

J. Am. Medical Informatics Assoc. (published)

CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data

Louis Mullie

Jonathan Afilalo

Patrick Archambault

Rima Bouchakri

Kip Brown

Yiorgos Alexandros Cavayas

Alexis F Turgeon

Denis Martineau

François Lamontagne

Martine Lebrasseur

Renald Lemieux

Jeffrey Li

Michaël Sauthier

Pascal St-Onge

An Tang

William Witteman

Michael Chassé

Abstract Objectives Distributed computations facilitate multi-institutional data analysis while avoiding the costs and complexity of data po… (see more)oling. Existing approaches lack crucial features, such as built-in medical standards and terminologies, no-code data visualizations, explicit disclosure control mechanisms, and support for basic statistical computations, in addition to gradient-based optimization capabilities. Materials and methods We describe the development of the Collaborative Data Analysis (CODA) platform, and the design choices undertaken to address the key needs identified during our survey of stakeholders. We use a public dataset (MIMIC-IV) to demonstrate end-to-end multi-modal FL using CODA. We assessed the technical feasibility of deploying the CODA platform at 9 hospitals in Canada, describe implementation challenges, and evaluate its scalability on large patient populations. Results The CODA platform was designed, developed, and deployed between January 2020 and January 2023. Software code, documentation, and technical documents were released under an open-source license. Multi-modal federated averaging is illustrated using the MIMIC-IV and MIMIC-CXR datasets. To date, 8 out of the 9 participating sites have successfully deployed the platform, with a total enrolment of >1M patients. Mapping data from legacy systems to FHIR was the biggest barrier to implementation. Discussion and conclusion The CODA platform was developed and successfully deployed in a public healthcare setting in Canada, with heterogeneous information technology systems and capabilities. Ongoing efforts will use the platform to develop and prospectively validate models for risk assessment, proactive monitoring, and resource usage. Further work will also make tools available to facilitate migration from legacy formats to FHIR and DICOM.

2023-12-21

J. Am. Medical Informatics Assoc. (published)

TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare

Ziyang Song

Qincheng Lu

Hao Xu

Mike He Zhu

Yue Li

Motivation: Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success in Natural Language Processing a… (see more)nd Computer Vision domains. However, the development of PTMs on healthcare time-series data is lagging behind. This underscores the limitations of the existing transformer-based architectures, particularly their scalability to handle large-scale time series and ability to capture long-term temporal dependencies. Methods: In this study, we present Timely Generative Pre-trained Transformer (TimelyGPT). TimelyGPT employs an extrapolatable position (xPos) embedding to encode trend and periodic patterns into time-series representations. It also integrates recurrent attention and temporal convolution modules to effectively capture global-local temporal dependencies. Materials: We evaluated TimelyGPT on two large-scale healthcare time series datasets corresponding to continuous biosignals and irregularly-sampled time series, respectively: (1) the Sleep EDF dataset consisting of over 1.2 billion timesteps; (2) the longitudinal healthcare administrative database PopHR, comprising 489,000 patients randomly sampled from the Montreal population. Results: In forecasting continuous biosignals, TimelyGPT achieves accurate extrapolation up to 6,000 timesteps of body temperature during the sleep stage transition, given a short look-up window (i.e., prompt) containing only 2,000 timesteps. For irregularly-sampled time series, TimelyGPT with a proposed time-specific inference demonstrates high top recall scores in predicting future diagnoses using early diagnostic records, effectively handling irregular intervals between clinical records. Together, we envision TimelyGPT to be useful in various health domains, including long-term patient health state forecasting and patient risk trajectory prediction. Availability: The open-sourced code is available at Github.

2023-11-29

ArXiv (preprint)

TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare

Ziyang Song

Qincheng Lu

Hao Xu

Mike He Zhu

Yue Li

2023-11-29

ArXiv (preprint)

Estimating the population effectiveness of interventions against COVID-19 in France: a modelling study

Iris Ganser

Jane M. Heffernan

M. Prague

Rodolphe Thiébaut

Background Non-pharmaceutical interventions (NPIs) and vaccines have been widely used to manage the COVID-19 pandemic. However, uncertainty … (see more)persists regarding the effectiveness of these interventions due to data quality issues, methodological challenges, and differing contextual factors. Accurate estimation of their effects is crucial for future epidemic preparedness. Methods To address this, we developed a population-based mechanistic model that includes the impact of NPIs and vaccines on SARS-CoV-2 transmission and hospitalization rates. Our statistical approach estimated all parameters in one step, accurately propagating uncertainty. We fitted the model to comprehensive epidemiological data in France from March 2020 to October 2021. With the same model, we simulated scenarios of vaccine rollout. Results The first lockdown was the most effective, reducing transmission by 84% (95% confidence interval (CI) 83-85). Subsequent lockdowns had diminished effectiveness (reduction of 74% (69-77) and 11% (9-18), respectively). A 6pm curfew was more effective than one at 8 pm (68% (66-69) vs. 48% (45-49) reduction), while school closures reduced transmission by 15% (12-18). In a scenario without vaccines before November 2021, we predicted 159,000 or 194% (95% prediction interval (PI) 74-424) more deaths and 1,488,000 or 340% (136-689) more hospitalizations. If a vaccine had been available after 100 days, over 71,000 deaths (16,507-204,249) and 384,000 (88,579-1,020,386) hospitalizations could have been averted. Conclusion Our results highlight the substantial impact of NPIs, including lockdowns and curfews, in controlling the COVID-19 pandemic. We also demonstrate the value of the 100 days objective of the CEPI initiative for vaccine availability.

2023-09-14

medRxiv (preprint)

Impact of a vaccine passport on first-dose SARS-CoV-2 vaccine coverage by age and area-level social determinants of health in the Canadian provinces of Quebec and Ontario: an interrupted time series analysis

Jorge Luis Flores Anato

Huiting Ma

M. Hamilton

Yiqing Xia

Sam Harper

Marc Brisson

Michael P. Hillmer

Kamil A. Malikov

Aidin Kerem

Reed Beall

Caroline E Wagner

Étienne Racine

S. Baral

Ève Dubé

Sharmistha Mishra

Mathieu Maheu-Giroux

2023-09-01

CMAJ Open (published)

The evolution of SARS-CoV-2 seroprevalence in Canada: a time-series study, 2020–2023

Tanya J. Murphy

Hanna Swail

Jaspreet Jain

Maureen Anderson

Philip Awadalla

Lesley Behl

P. Brown

C. Charlton

Karen Colwill

S. Drews

A. Gingras

Deena Hinshaw

P. Jha

J. Kanji

Victoria A. Kirsh

Amanda Lang

Marc-andré Langlois

Stephen Lee

Antoine Lewin

Sheila F O’Brien … (see 10 more)

Chantale Pambrun

Kimberly Skead

David A. Stephens

Derek R. Stein

G. Tipples

Paul G. Van Caeseele

Timothy Grant Evans

Olivia Oxlade

Bruce D. Mazer

Background: During the first year of the COVID-19 pandemic, the proportion of reported cases of COVID-19 among Canadians was under 6%. Altho… (see more)ugh high vaccine coverage was achieved in Canada by fall 2021, the Omicron variant caused unprecedented numbers of infections, overwhelming testing capacity and making it difficult to quantify the trajectory of population immunity. Methods: Using a time-series approach and data from more than 900 000 samples collected by 7 research studies collaborating with the COVID-19 Immunity Task Force (CITF), we estimated trends in SARS-CoV-2 seroprevalence owing to infection and vaccination for the Canadian population over 3 intervals: prevaccination (March to November 2020), vaccine roll-out (December 2020 to November 2021), and the arrival of the Omicron variant (December 2021 to March 2023). We also estimated seroprevalence by geographical region and age. Results: By November 2021, 9.0% (95% credible interval [CrI] 7.3%–11%) of people in Canada had humoral immunity to SARS-CoV-2 from an infection. Seroprevalence increased rapidly after the arrival of the Omicron variant — by Mar. 15, 2023, 76% (95% CrI 74%–79%) of the population had detectable antibodies from infections. The rapid rise in infection-induced antibodies occurred across Canada and was most pronounced in younger age groups and in the Western provinces: Manitoba, Saskatchewan, Alberta and British Columbia. Interpretation: Data up to March 2023 indicate that most people in Canada had acquired antibodies against SARS-CoV-2 through natural infection and vaccination. However, given variations in population seropositivity by age and geography, the potential for waning antibody levels, and new variants that may escape immunity, public health policy and clinical decisions should be tailored to local patterns of population immunity.

2023-08-13

Canadian Medical Association Journal (published)

Protective effectiveness of previous SARS-CoV-2 infection and hybrid immunity against the omicron variant and severe disease: a systematic review and meta-regression

Niklas Bobrovitz

Harriet Ware

Xiaomeng Ma

Zihan Li

Reza Hosseini

Christian Cao

Anabel Selemon

Mairead Whelan

Zahra Premji

Hanane Issa

Brianna Cheng

Laith J Abu Raddad

Maria D Van Kerkhove

Vanessa Piechotta

Melissa M Higdon

Annelies Wilder-Smith

Isabel Bergeri

Daniel R Feikin

Rahul K. Arora … (see 2 more)

Minal K Patel

Lorenzo Subissi

2023-05-01

The Lancet Infectious Diseases (published)

Proactive Contact Tracing

Prateek Gupta

Tegan Maharaj

Martin Weiss

Nasim Rahaman

Hannah Alsdurf

Nanor Minoyan

Soren Harnois-Leblanc

Joanna Merckx

andrew williams

Victor Schmidt

Pierre-Luc St-Charles

Akshay Patel

Yang Zhang

Chris Pal

Bernhard Schölkopf

Yoshua Bengio

2023-03-13

PLOS Digital Health (published)