Publications

Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods
Taylor Bolt
Jason S. Nomi
Danilo Bzdok
Lucina Q. Uddin
Methods for data analysis in the biomedical, life, and social (BLS) sciences are developing at a rapid pace. At the same time, there is incr… (see more)easing concern that education in quantitative methods is failing to adequately prepare students for contemporary research. These trends have led to calls for educational reform to undergraduate and graduate quantitative research method curricula. We argue that such reform should be based on data-driven insights into within- and cross-disciplinary use of analytic methods. Our survey of peer-reviewed literature analyzed approximately 1.3 million openly available research articles to monitor the cross-disciplinary mentions of analytic methods in the past decade. We applied data-driven text mining analyses to the “Methods” and “Results” sections of a large subset of this corpus to identify trends in analytic method mentions shared across disciplines, as well as those unique to each discipline. We found that the t test, analysis of variance (ANOVA), linear regression, chi-squared test, and other classical statistical methods have been and remain the most mentioned analytic methods in biomedical, life science, and social science research articles. However, mentions of these methods have declined as a percentage of the published literature between 2009 and 2020. On the other hand, multivariate statistical and machine learning approaches, such as artificial neural networks (ANNs), have seen a significant increase in the total share of scientific publications. We also found unique groupings of analytic methods associated with each BLS science discipline, such as the use of structural equation modeling (SEM) in psychology, survival models in oncology, and manifold learning in ecology. We discuss the implications of these findings for education in statistics and research methods, as well as within- and cross-disciplinary collaboration.
2021-06-30
PLoS Biology (published)
doi.org
Enjeux juridiques propres au modèle émergent des patients accompagnateurs dans les milieux de soins au Québec (Legal Issues Arising from the Emerging Model of Accompanying Patients in the Quebec Healthcare System)
Léa Boutrouille
Catherine Régis
Marie-Pascale Pomey
2021-06-30
Social Science Research Network (published)
doi.org
Guest Editorial Explainable AI: Towards Fairness, Accountability, Transparency and Trust in Healthcare
Arash Shaban-Nejad
Martin Michalowski
John S. Brownstein
David L Buckeridge
2021-06-30
IEEE Journal of Biomedical and Health Informatics (published)
doi.org
Learning a Universal Template for Few-shot Dataset Generalization
Eleni Triantafillou
Hugo Larochelle
Richard Zemel
Vincent Dumoulin
2021-06-30
Proceedings of the 38th International Conference on Machine Learning (published)
proceedings.mlr.press
proceedings.mlr.press
Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards
Susan Amin
Maziar Gomrokchi
Hossein Aboutalebi
Harsh Satija
Doina Precup
A major challenge in reinforcement learning is the design of exploration strategies, especially for environments with sparse reward structur… (see more)es and continuous state and action spaces. Intuitively, if the reinforcement signal is very scarce, the agent should rely on some form of short-term memory in order to cover its environment efficiently. We propose a new exploration method, based on two intuitions: (1) the choice of the next exploratory action should depend not only on the (Markovian) state of the environment, but also on the agent's trajectory so far, and (2) the agent should utilize a measure of spread in the state space to avoid getting stuck in a small region. Our method leverages concepts often used in statistical physics to provide explanations for the behavior of simplified (polymer) chains in order to generate persistent (locally self-avoiding) trajectories in state space. We discuss the theoretical properties of locally self-avoiding walks and their ability to provide a kind of short-term memory through a decaying temporal correlation within the trajectory. We provide empirical evaluations of our approach in a simulated 2D navigation task, as well as higher-dimensional MuJoCo continuous control locomotion tasks with sparse rewards.
2021-06-30
Proceedings of the 38th International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
Soumyasundar Pal
Liheng Ma
Yingxue Zhang
Mark J. Coates
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks. Many classical statistical mod… (see more)els often fall short in handling the complexity and high non-linearity present in time-series data. Recent advances in deep learning allow for better modelling of spatial and temporal dependencies. While most of these models focus on obtaining accurate point forecasts, they do not characterize the prediction uncertainty. In this work, we consider the time-series data as a random realization from a nonlinear state-space model and target Bayesian inference of the hidden states for probabilistic forecasting. We use particle flow as the tool for approximating the posterior distribution of the states, as it is shown to be highly effective in complex, high-dimensional settings. Thorough experimentation on several real world time-series datasets demonstrates that our approach provides better characterization of uncertainty while maintaining comparable accuracy to the state-of-the art point forecasting methods.
2021-06-30
Proceedings of the 38th International Conference on Machine Learning (published)
proceedings.mlr.press
proceedings.mlr.press
Smart About Meds (SAM): a pilot randomized controlled trial of a mobile application to improve medication adherence following hospital discharge
Bettina Habib
David Buckeridge
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… (see more)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 (published)
doi.org
Measures of balance in combinatorial optimization
Philippe Olivier
Andrea Lodi
Gilles Pesant
2021-06-24
4OR (published)
doi.org
Deep learning for AI
Yoshua Bengio
Yann Lecun
Geoffrey Hinton
How can neural networks learn the rich internal representations required for difficult tasks such as recognizing objects or understanding la… (see more)nguage?
2021-06-20
Communications of the ACM (published)
doi.org
Large-Scale Intrinsic Functional Brain Organization Emerges from Three Canonical Spatiotemporal Patterns
Taylor Bolt
Jason S. Nomi
Danilo Bzdok
Catie Chang
B.T. Yeo
Lucina Q. Uddin
Shella Keilholz
2021-06-19
(published)
doi.org
Digitizing a sustainable future
Lucia A. Reisch
Lucas Joppa
Peter Howson
Artur Gil
Panayiota Alevizou
Nina Michaelidou
Ruby Appiah-Campbell
Tilman Santarius
Susanne Köhler
Massimo Pizzol
Pia-Johanna Schweizer
Dipti Srinivasan
Lynn H. Kaack
Priya L. Donti
David Rolnick
2021-06-17
One Earth (published)
doi.org
The Cost of Untracked Diversity in Brain-Imaging Prediction
Oualid Benkarim
Casey Paquola
Bo-yong Park
Valeria Kebets
Seok-Jun Hong
Reinder Vos de Wael
Shaoshi Zhang
B.T. Thomas Yeo
Michael Eickenberg
Tian Ge
Jean-Baptiste Poline
Boris Bernhardt
Danilo Bzdok
Brain-imaging research enjoys increasing adoption of supervised machine learning for singlesubject disease classification. Yet, the success … (see more)of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population stratification. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in two separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n=297) and the Healthy Brain Network (HBN, n=551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially to the default mode network. Our collective findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.
2021-06-16
bioRxiv (preprint)
doi.org