Audrey Durand

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

Data harmonization for Advancing research on Personalized Rehabilitation Interventions for Patients with Traumatic Brain Injury and Stroke: A proof of concept

Dorra Rakia Allegue

Despoina Petsani

Nathalie Ponthon

Evdokimos Konstantinidis

Panagiotis Bamidis

Eva Kehayia

Sara Ahmed

Stroke and traumatic brain injury (TBI) are leading causes of morbidity and mortality, affecting survivors’ mobility and social participat… (voir plus)ion. Although personalized interventions could positively impact survivors' recovery, the effectiveness of such interventions remains unclear. Open-access data repositories can provide access to multiple shared data which could help uncover new evidence of effective interventions; however, harmonizing data between different studies requires many steps to make it possible given the various methods of data collection, intervention characteristics and population sociodemographic profile. This proof-of-concept study aimed to describe the steps and anchors that contributed to the development of guiding frameworks to harmonize data across different studies. Data were extracted from the Federal Interagency Traumatic Brain Injury Research (FITBIR) repository and stored on an online cloud platform. The outcome measures were mapped to mobility determinants using the International Classification of Functioning, Disability, and Health (ICF) and Webber framework. The intervention's effect was categorized according to the Minimal Clinically Important Difference (MCID)s of the measures administered. The study proposed a novel framework for intervention features, which aims to enhance our understanding of the mechanisms of action and potential impact of rehabilitation interventions. The framework classified interventions based on their nature, context, specific body systems, dosage, caregiver assistance, and behaviour change strategies. In conclusion, this study demonstrated the feasibility of harmonizing data extracted from different sources in the FITBIR repository. Leveraging existing open databases offers tremendous opportunities to advance research on personalized interventions for patients with TBI and stroke and inform decision-making during transitions.

2024-06-26

Petra (publié)

On shallow planning under partial observability

Randy Lefebvre

2024-05-31

rl-conference.cc/RLC/2024/Workshop/Deployable_RL (publié)

Neural Active Learning Meets the Partial Monitoring Framework

Maxime Heuillet

Ola Ahmad

We focus on the online-based active learning (OAL) setting where an agent operates over a stream of observations and trades-off between the … (voir plus)costly acquisition of information (labelled observations) and the cost of prediction errors. We propose a novel foundation for OAL tasks based on partial monitoring, a theoretical framework specialized in online learning from partially informative actions. We show that previously studied binary and multi-class OAL tasks are instances of partial monitoring. We expand the real-world potential of OAL by introducing a new class of cost-sensitive OAL tasks. We propose NeuralCBP, the first PM strategy that accounts for predictive uncertainty with deep neural networks. Our extensive empirical evaluation on open source datasets shows that NeuralCBP has favorable performance against state-of-the-art baselines on multiple binary, multi-class and cost-sensitive OAL tasks.

2024-04-26

auai.org/UAI/2024/Conference (poster)

Neural Active Learning Meets the Partial Monitoring Framework

Maxime Heuillet

Ola Ahmad

2024-04-26

auai.org/UAI/2024/Conference (poster)

Development of AI-assisted microscopy frameworks through realistic simulation in pySTED

Anthony Bilodeau

Albert Michaud-Gagnon

Julia Chabbert

Benoit Turcotte

Jörn Heine

Flavie Lavoie-Cardinal

The integration of artificial intelligence into microscopy systems significantly enhances performance, optimizing both the image acquisition… (voir plus) and analysis phases. Development of artificial intelligence (AI)-assisted super-resolution microscopy is often limited by the access to large biological datasets, as well as by the difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic STED simulation platform, pySTED, for the development and deployment of AI-strategies for super-resolution microscopy. The simulation environment provided by pySTED allows the augmentation of data for the training of deep neural networks, the development of online optimization strategies, and the training of reinforcement learning models, that can be deployed successfully on a real microscope.

2024-03-29

bioRxiv (prépublication)

Randomized Confidence Bounds for Stochastic Partial Monitoring

Maxime Heuillet

Ola Ahmad

2024-02-07

ArXiv (prépublication)

arxiv.org

GrowSpace: A reinforcement learning environment for plant architecture

Yasmeen Hitti

Ionelia Buzatu

Manuel Del Verme

Florian Golemo

Mark Lefsrud

2024-02-01

Computers and Electronics in Agriculture (publié)

Deep reinforcement learning for continuous wood drying production line control

François-Alexandre Tremblay

Michael Morin

Philippe Marier

Jonathan Gaudreault

2024-01-01

Computers in Industry (publié)

Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract)

Charles Bourbeau

This work investigates the evolution of latent space when deep learning models are trained incrementally in non-stationary environments that… (voir plus) stem from concept drift. We propose a methodology for visualizing the incurred change in latent representations. We further show that classes not targeted by concept drift can be negatively affected, suggesting that the observation of all classes during learning may regularize the latent space.

2023-06-26

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

Chronic Stress Exposure Alters the Gut Barrier: Sex-Specific Effects on Microbiota and Jejunum Tight Junctions

Ellen Doney

Laurence Dion-Albert

Francois Coulombe-Rozon

Natasha Osborne

Renaud Bernatchez

Sam E.J. Paton

Fernanda Neutzling Kaufmann

Roseline Olory Agomma

José L. Solano

Raphael Gaumond

Katarzyna A. Dudek

Joanna Kasia Szyszkowicz

Manon Lebel

Alain Doyen

Flavie Lavoie-Cardinal

Marie-Claude Audet

Caroline Menard

2023-05-01

Biological Psychiatry Global Open Science (publié)

The Influence of Age, Sex, and Socioeconomic Status on Glycemic Control Among People With Type 1 and Type 2 Diabetes in Canada: Patient-Led Longitudinal Retrospective Cross-sectional Study With Multiple Time Points of Measurement

Seyedmostafa Mousavi

Dana Tannenbaum Greenberg

Ruth Ndjaboué

Michelle Greiver

Olivia Drescher

Selma Chipenda Dansokho

Denis Boutin

Jean-Marc Chouinard

Sylvie Dostie

Robert Fenton

Marley Greenberg

Jonathan McGavock

Adhiyat Najam

Monia Rekik

Tom Weisz

Donald J Willison

Holly O Witteman

2023-04-27

JMIR Diabetes (publié)