David Buckeridge

Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success in Natural Language Processing and Computer … (voir plus)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. 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. We evaluated TimelyGPT on two large-scale healthcare time series datasets corresponding to continuous biosignals and irregularly-sampled time series, respectively. Our experiments show that during pre-training, TimelyGPT excels in learning time-series representations from continuously monitored biosignals and irregularly-sampled time series data commonly observed in longitudinal electronic health records (EHRs). 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 a broad spectrum of health domains, including long-term patient health state forecasting and patient risk trajectory prediction.

2025-10-13

Health Information Science and Systems (publié)

arxiv.org

TrajGPT: Irregular Time-Series Representation Learning of Health Trajectory

Ziyang Song

Qincheng Lu

He Zhu

In the healthcare domain, time-series data are often irregularly sampled with varying intervals through outpatient visits, posing challenges… (voir plus) for existing models designed for equally spaced sequential data. To address this, we propose Trajectory Generative Pre-trained Transformer (TrajGPT) for representation learning on irregularly-sampled healthcare time series. TrajGPT introduces a novel Selective Recurrent Attention (SRA) module that leverages a data-dependent decay to adaptively filter irrelevant past information. As a discretized ordinary differential equation (ODE) framework, TrajGPT captures underlying continuous dynamics and enables a time-specific inference for forecasting arbitrary target timesteps without auto-regressive prediction. Experimental results based on the longitudinal EHR data PopHR from Montreal health system and eICU from PhysioNet showcase TrajGPT's superior zero-shot performance in disease forecasting, drug usage prediction, and sepsis detection. The inferred trajectories of diabetic and cardiac patients reveal meaningful comorbidity conditions, underscoring TrajGPT as a useful tool for forecasting patient health evolution.

2024-12-31

IEEE Journal of Biomedical and Health Informatics (inconnu)

MixEHR-Nest: Identifying Subphenotypes within Electronic Health Records through Hierarchical Guided-Topic Modeling

Ruohan Wang

Zilong Wang

Ziyang Song

Automatic subphenotyping from electronic health records (EHRs)provides numerous opportunities to understand diseases with unique subgroups a… (voir plus)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.

2024-11-21

Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (publié)

arxiv.org

Impact of a vaccine passport on first-dose COVID-19 vaccine coverage by age and area-level social determinants in the Canadian provinces of Québec and Ontario: an interrupted time series analysis

Jorge Luis Flores Anato

Huiting Ma

Mackenzie A. Hamilton

Yiqing Xia

Sam Harper

Marc Brisson

Michael P. Hillmer

Kamil Malikov

Aidin Kerem

Reed Beall

Caroline E Wagner

Étienne Racine

Stefan Baral

Ève Dubé

Sharmistha Mishra

Mathieu Maheu-Giroux

In Canada, all provinces implemented vaccine passports in 2021 to increase vaccine uptake and reduce transmission in non-essential indoor sp… (voir plus)aces. We evaluate the impact of vaccine passport policies on first-dose COVID-19 vaccination coverage by age, area-level income and proportion racialized. We performed interrupted time-series analyses using vaccine registry data linked to census information in Québec and Ontario (20.5 million people ≥12 years; unit of analysis: dissemination area). We fit negative binomial regressions to weekly first-dose vaccination, using a natural spline to capture pre-announcement trends, adjusting for baseline vaccination coverage (start: July 3 rd ; end: October 23 rd Québec, November 13 th Ontario). We obtain counterfactual vaccination rates and coverage, and estimated vaccine passports’ impact on vaccination coverage (absolute) and new vaccinations (relative). In both provinces, pre-announcement first-dose vaccination coverage was 82% (≥12 years). The announcement resulted in estimated increases in vaccination coverage of 0.9 percentage points (p.p.;95%CI:0.4-1.2) in Québec and 0.7 p.p. (95%CI:0.5-0.8) in Ontario. In relative terms, these increases correspond to 23% (95%CI:10-36%) and 19% (95%CI:15-22%) more vaccinations. The impact was larger among people aged 12-39 (1-2 p.p.). There was little variability in the absolute impact by area-level income or proportion racialized in either province. In the context of high baseline vaccine coverage across two provinces, the announcement of vaccine passports led to a small impact on first-dose coverage, with little impact on reducing economic and racial inequities in vaccine coverage. Findings suggest the need for other policies to further increase vaccination coverage among lower-income and more racialized neighbourhoods and communities. Vaccine passport policies increased COVID-19 vaccination coverage by approximately 1 percentage point (19 to 23% increase in vaccinations) in Québec and Ontario, Canada. Although vaccine passport policies increased vaccination coverage, absolute gains were limited in the context of high prior vaccine coverage. Vaccine passports had little impact on reducing economic and racial inequities in vaccine coverage.

2023-08-31

CMAJ Open (publié)

BioCaster in 2021: automatic disease outbreaks detection from global news media

Zaiqiao Meng

Anya Okhmatovskaia

Maxime Polleri

Yannan Shen

Guido Powell

Zihao Fu

Iris Ganser

Meiru Zhang

Nicholas B. King

Nigel Collier

SUMMARY: BioCaster was launched in 2008 to provide an ontology-based text mining system for early disease detection from open news sources. … (voir plus)Following a 6-year break, we have re-launched the system in 2021. Our goal is to systematically upgrade the methodology using state-of-the-art neural network language models, whilst retaining the original benefits that the system provided in terms of logical reasoning and automated early detection of infectious disease outbreaks. Here, we present recent extensions such as neural machine translation in 10 languages, neural classification of disease outbreak reports and a new cloud-based visualization dashboard. Furthermore, we discuss our vision for further improvements, including combining risk assessment with event semantics and assessing the risk of outbreaks with multi-granularity. We hope that these efforts will benefit the global public health community. AVAILABILITY AND IMPLEMENTATION: BioCaster web-portal is freely accessible at http://biocaster.org.

2022-07-27

Bioinformatics (publié)

Supervised multi-specialist topic model with applications on large-scale electronic health record data

Ziyang Song

Xavier Sumba Toral

Yixin Xu

Aihua Liu

Liming Guo

Guido Powell

Aman Verma

Ariane Marelli

Motivation: Electronic health record (EHR) data provides a new venue to elucidate disease comorbidities and latent phenotypes for precision … (voir plus)medicine. To fully exploit its potential, a realistic data generative process of the EHR data needs to be modelled. We present MixEHR-S to jointly infer specialist-disease topics from the EHR data. As the key contribution, we model the specialist assignments and ICD-coded diagnoses as the latent topics based on patient's underlying disease topic mixture in a novel unified supervised hierarchical Bayesian topic model. For efficient inference, we developed a closed-form collapsed variational inference algorithm to learn the model distributions of MixEHR-S. We applied MixEHR-S to two independent large-scale EHR databases in Quebec with three targeted applications: (1) Congenital Heart Disease (CHD) diagnostic prediction among 154,775 patients; (2) Chronic obstructive pulmonary disease (COPD) diagnostic prediction among 73,791 patients; (3) future insulin treatment prediction among 78,712 patients diagnosed with diabetes as a mean to assess the disease exacerbation. In all three applications, MixEHR-S conferred clinically meaningful latent topics among the most predictive latent topics and achieved superior target prediction accuracy compared to the existing methods, providing opportunities for prioritizing high-risk patients for healthcare services. MixEHR-S source code and scripts of the experiments are freely available at https://github.com/li-lab-mcgill/mixehrS

2021-07-31

Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (publié)

arxiv.org

Smart About Meds (SAM): a pilot randomized controlled trial of a mobile application to improve medication adherence following hospital discharge

Bettina Habib

Melissa Bustillo

Santiago Nicolas Marquez

Manish Thakur

Thai Tran

Daniala L. Weir

Robyn Tamblyn

The objectives of this pilot study were (1) to assess the feasibility of a larger evaluation of Smart About Meds (SAM), a patient-centered m… (voir plus)edication management mobile application, and (2) to evaluate SAM’s potential to improve outcomes of interest, including adherence to medication changes made at hospital discharge and the occurrence of adverse events. We conducted a pilot randomized controlled trial among patients discharged from internal medicine units of an academic health center between June 2019 and March 2020. Block randomization was used to randomize patients to intervention (received access to SAM at discharge) or control (received usual care). Patients were followed for 30 days post-discharge, during which app use was recorded. Pharmacy claims data were used to measure adherence to medication changes made at discharge, and physician billing data were used to identify emergency department visits and hospital readmissions during follow-up. Forty-nine patients were eligible for inclusion in the study at hospital discharge (23 intervention, 26 control). In the 30 days of post-discharge, 15 (65.2%) intervention patients used the SAM app. During this period, intervention patients adhered to a larger proportion of medication changes (83.7%) than control patients (77.8%), including newly prescribed medications (72.7% vs 61.7%) and dose changes (90.9% vs 81.8%). A smaller proportion of intervention patients (8.7%) were readmitted to hospital during follow-up than control patients (15.4%). The high uptake of SAM among intervention patients supports the feasibility of a larger trial. Results also suggest that SAM has the potential to enhance adherence to medication changes and reduce the risk of downstream adverse events. This hypothesis needs to be tested in a larger trial. Clinicaltrials.gov, registration number NCT04676165.

2021-06-30

JAMIA Open (publié)

Predicting Infectiousness for Proactive Contact Tracing

Prateek Gupta

Nasim Rahaman

Pierre-Luc St. Charles

Hannah Alsdurf

Olexa Bilaniuk

Gaétan Marceau-Caron

Pierre-Luc Carrier

Joumana Ghosn

Satya Ortiz-Gagné

Chris Pal

Irina Rish

Bernhard Schölkopf … (voir 3 de plus)

Abhinav Sharma

Jian Tang

Andrew Williams

The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdo… (voir plus)wns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). We find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.

2020-12-31

ICLR (publié)

openreview.net

COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing

Prateek Gupta

Tegan Maharaj

Martin Weiss

Nasim Rahaman

Hannah Alsdurf

Abhinav Sharma

Nanor Minoyan

Soren Harnois Leblanc

Victor Schmidt

Pierre-Luc St. Charles

Akshay Patel

David Buckeridge … (voir 9 de plus)

Joumana Ghosn

Yang Zhang

Bernhard Schölkopf

Jian Tang

Irina Rish

Christopher Pal

Joanna Merckx

Eilif B. Muller

Yoshua Bengio

The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and vari… (voir plus)ous digital contact tracing (DCT) methods have emerged as a component of the solution. In order to make informed public health choices, there is a need for tools which allow evaluation and comparison of DCT methods. We introduce an agent-based compartmental simulator we call COVI-AgentSim, integrating detailed consideration of virology, disease progression, social contact networks, and mobility patterns, based on parameters derived from empirical research. We verify by comparing to real data that COVI-AgentSim is able to reproduce realistic COVID-19 spread dynamics, and perform a sensitivity analysis to verify that the relative performance of contact tracing methods are consistent across a range of settings. We use COVI-AgentSim to perform cost-benefit analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features. We find all DCT methods consistently reduce the spread of the disease, and that the advantage of FCT over BCT is maintained over a wide range of adoption rates. Feature-based methods of contact tracing avert more disability-adjusted life years (DALYs) per socioeconomic cost (measured by productive hours lost). Our results suggest any DCT method can help save lives, support re-opening of economies, and prevent second-wave outbreaks, and that FCT methods are a promising direction for enriching BCT using self-reported symptoms, yielding earlier warning signals and a significantly reduced spread of the virus per socioeconomic cost.

2020-10-01

OpenReview.net/Anonymous_Preprint (inconnu)

openreview.net

Both New and Chronic Potentially Inappropriate Medications Continued at Hospital Discharge Are Associated With Increased Risk of Adverse Events

Daniala L. Weir

Todd C. Lee

Emily G. McDonald

Aude Motulsky

Michal Abrahamowicz

Steven Morgan

Robyn Tamblyn

Admission to hospital provides the opportunity to review patient medications; however, the extent to which the safety of drug regimens chang… (voir plus)es after hospitalization is unclear. To estimate the number of potentially inappropriate medications (PIMs) prescribed to patients at hospital discharge and their association with the risk of adverse events 30 days after discharge. Prospective cohort study. Tertiary care hospitals within the McGill University Health Centre Network in Montreal, Quebec, Canada. Patients from internal medicine, cardiac, and thoracic surgery, aged 65 years and older, admitted between October 2014 and November 2016. Abstracted chart data were linked to provincial health databases. PIMs were identified using AGS (American Geriatrics Society) Beers Criteria®, STOPP, and Choosing Wisely statements. Multivariable logistic regression and Cox models were used to assess the association between PIMs and adverse events. Of 2,402 included patients, 1,381 (57%) were male; median age was 76 years (interquartile range [IQR] = 70‐82 years); and eight discharge medications were prescribed (IQR = 2‐8). A total of 1,576 (66%) patients were prescribed at least one PIM at discharge; 1,176 (49%) continued a PIM from prior to admission, and 755 (31%) were prescribed at least one new PIM. In the 30 days after discharge, 218 (9%) experienced an adverse drug event (ADE) and 862 (36%) visited the emergency department (ED), were rehospitalized, or died. After adjustment, each additional new PIM and continued community PIM were respectively associated with a 21% (odds ratio [OR] = 1.21; 95% confidence interval [CI] = 1.01‐1.45) and a 10% (OR = 1.10; 95% CI = 1.01‐1.21) increased odds of ADEs. They were also respectively associated with a 13% (hazard ratio [HR] = 1.13; 95% CI = 1.03‐1.26) and a 5% (HR = 1.05; 95% CI = 1.00‐1.10) increased risk of ED visits, rehospitalization, and death. Two in three hospitalized patients were prescribed a PIM at discharge, and increasing numbers of PIMs were associated with an increased risk of ADEs and all‐cause adverse events. Improving hospital prescribing practices may reduce the frequency of PIMs and associated adverse events. J Am Geriatr Soc 68:1184–1192, 2020.

2020-03-30

Journal of the American Geriatrics Society (publié)