Portrait of Audrey Durand

Audrey Durand

Associate Academic Member
Canada CIFAR AI Chair
Assistant Professor, Université Laval, Department of Computer Science and Software Engineering
Research Topics
Online Learning
Reinforcement Learning
Website
Google Scholar
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Biography

Audrey Durand is an assistant professor in the Department of Computer Science and Software Engineering and in the Department of Electrical and Computer Engineering at Université Laval.

She specializes in algorithms that learn through interaction with their environment using reinforcement learning, and is particularly interested in leveraging these approaches in health-related applications.

Current Students

Dorra Allegue
Postdoctorate - Université Laval
Mathieu Godbout
PhD - Université Laval
Github
Maxime Heuillet
PhD - Université Laval
Yasmeen Hitti
PhD - McGill University
Co-supervisor :
Gauthier Gidel
Alexandre Larouche
PhD - Université Laval
Github
Google Scholar
Randy Lefebvre
Master's Research - Université Laval
Kaoutar Mimouni
Master's Research - Université Laval
Hadi Moazen
PhD - Université Laval

Publications

Association Rules Mining with Auto-Encoders
Th'eophile Berteloot
Richard Khoury
Audrey Durand
Association rule mining is one of the most studied research fields of data mining, with applications ranging from grocery basket problems to… (see more) explainable classification systems. Classical association rule mining algorithms have several limitations, especially with regards to their high execution times and number of rules produced. Over the past decade, neural network solutions have been used to solve various optimization problems, such as classification, regression or clustering. However there are still no efficient way association rules using neural networks. In this paper, we present an auto-encoder solution to mine association rule called ARM-AE. We compare our algorithm to FP-Growth and NSGAII on three categorical datasets, and show that our algorithm discovers high support and confidence rule set and has a better execution time than classical methods while preserving the quality of the rule set produced.
2023-04-26
ArXiv (preprint)
doi.org
arxiv.org
Reducing touching eyes, nose and mouth ('T-zone') to reduce the spread of infectious disease: A prospective study of motivational, volitional and non-reflective predictors.
Mackenzie Wilson
Zachary M. van Allen
Jeremy M. Grimshaw
Jamie C. Brehaut
Audrey Durand
Jean‐François Lalonde
Douglas G. Manuel
Susan Michie
Robert West
Justin Presseau
BACKGROUND The route into the body for many pathogens is through the eyes, nose and mouth (i.e., the 'T-zone') via inhalation or fomite-base… (see more)d transfer during face touching. It is important to understand factors that are associated with touching the T-zone to inform preventive strategies. PURPOSE To identify theory-informed predictors of intention to reduce facial 'T-zone' touching and self-reported 'T-zone' touching. METHODS We conducted a nationally representative prospective questionnaire study of Canadians. Respondents were randomized to answer questions about touching their eyes, nose, or mouth with a questionnaire assessing 11 factors from an augmented Health Action Process Approach at baseline: intention, outcome expectancies, risk perception, individual severity, self-efficacy, action planning, coping planning, social support, automaticity, goal facilitation and stability of context. At 2-week follow-up, we assessed HAPA-based indicators of self-regulatory activities (awareness of standards, effort, self-monitoring) and self-reported behaviour (primary dependent variable). RESULTS Of 656 Canadian adults recruited, 569 responded to follow-up (87% response rate). Across all areas of the 'T-zone', outcome expectancy was the strongest predictor of intention to reduce facial 'T-zone' touching, while self-efficacy was a significant predictor for only the eyes and mouth. Automaticity was the strongest predictor of behaviour at the 2-week follow-up. No sociodemographic or psychological factors predicted behaviour, with the exception of self-efficacy, which negatively predicted eye touching. CONCLUSION Findings suggest that focusing on reflective processes may increase intention to reduce 'T-zone' touching, while reducing actual 'T-zone' touching may require strategies that address the automatic nature of this behaviour.
2023-03-30
British Journal of Health Psychology (published)
doi.org
Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients
Rupali Bhati
Jennifer Jones
Audrey Durand
Cancer treatment is an arduous process for patients and causes many side-effects during and post-treatment. The treatment can affect almost … (see more)all body systems and result in pain, fatigue, sleep disturbances, cognitive impairments, etc. These conditions are often under-diagnosed or under-treated. In this paper, we use patient data to predict the evolution of their symptoms such that treatment-related impairments can be prevented or effects meaningfully ameliorated. The focus of this study is on predicting the pain and tiredness level of a patient post their diagnosis. We implement an interpretable decision tree based model called LightGBM on real-world patient data consisting of 20163 patients. There exists a class imbalance problem in the dataset which we resolve using the oversampling technique of SMOTE. Our empirical results show that the value of the previous level of a symptom is a key indicator for prediction and the weighted average deviation in prediction of pain level is 3.52 and of tiredness level is 2.27.
2023-02-19
ArXiv (preprint)
doi.org
arxiv.org
Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy
Alexandre Larouche
Audrey Durand
Richard Khoury
Caroline Sirois
2022-12-10
ArXiv (preprint)
doi.org
arxiv.org
Informing the development of an outcome set and banks of items to measure mobility among individuals with acquired brain injury using natural language processing
Rehab Alhasani
Mathieu Godbout
Audrey Durand
Claudine Auger
Anouk Lamontagne
Sara Ahmed
2022-12-09
BMC Neurology (published)
doi.org
Performative Prediction in Time Series: A Case Study
Rupali Bhati
Jennifer Jones
David Langelier
Anthony Reiman
Jonathan Greenland
Kristin Campbell
Audrey Durand
2022-12-02
NeurIPS.cc/2022/Workshop/TS4H (poster)
openreview.net
openreview.net
Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract)
Renaud Bernatchez
Audrey Durand
Flavie Lavoie-Cardinal
2022-06-28
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Rehabilitation: Protocol for Evaluation and Rehabilitation of Mobility Across Continuums of Care
Sara Ahmed
Philippe Archambault
Claudine Auger
Audrey Durand
Joyce Fung
Eva Kehayia
Anouk Lamontagne
Annette Majnemer
Sylvie Nadeau
Joelle Pineau
Alain Ptito
Bonnie Swaine
Background Rapid advances in technologies over the past 10 years have enabled large-scale biomedical and psychosocial rehabilitation researc… (see more)h to improve the function and social integration of persons with physical impairments across the lifespan. The Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies (BRILLIANT) in community mobility rehabilitation aims to generate evidence-based research to improve rehabilitation for individuals with acquired brain injury (ABI). Objective This study aims to (1) identify the factors limiting or enhancing mobility in real-world community environments (public spaces, including the mall, home, and outdoors) and understand their complex interplay in individuals of all ages with ABI and (2) customize community environment mobility training by identifying, on a continuous basis, the specific rehabilitation strategies and interventions that patient subgroups benefit from most. Here, we present the research and technology plan for the BRILLIANT initiative. Methods A cohort of individuals, adults and children, with ABI (N=1500) will be recruited. Patients will be recruited from the acute care and rehabilitation partner centers within 4 health regions (living labs) and followed throughout the continuum of rehabilitation. Participants will also be recruited from the community. Biomedical, clinician-reported, patient-reported, and brain imaging data will be collected. Theme 1 will implement and evaluate the feasibility of collecting data across BRILLIANT living labs and conduct predictive analyses and artificial intelligence (AI) to identify mobility subgroups. Theme 2 will implement, evaluate, and identify community mobility interventions that optimize outcomes for mobility subgroups of patients with ABI. Results The biomedical infrastructure and equipment have been established across the living labs, and development of the clinician- and patient-reported outcome digital solutions is underway. Recruitment is expected to begin in May 2022. Conclusions The program will develop and deploy a comprehensive clinical and community-based mobility-monitoring system to evaluate the factors that result in poor mobility, and develop personalized mobility interventions that are optimized for specific patient subgroups. Technology solutions will be designed to support clinicians and patients to deliver cost-effective care and the right intervention to the right person at the right time to optimize long-term functional potential and meaningful participation in the community. International Registered Report Identifier (IRRID) PRR1-10.2196/12506
2022-06-01
JMIR Research Protocols (published)
doi.org
Contextual bandit optimization of super-resolution microscopy
Anthony Bilodeau
Renaud Bernatchez
Albert Michaud-Gagnon
Flavie Lavoie-Cardinal
Audrey Durand
2022-05-27
Applied Informatics (published)
doi.org
Microscopy analysis neural network to solve detection, enumeration and segmentation from image-level annotations
Anthony Bilodeau
Constantin V.L. Delmas
Martin Parent
Paul De Koninck
Audrey Durand
Flavie Lavoie-Cardinal
2022-04-14
Nature Machine Intelligence (published)
doi.org
Microscopy analysis neural network to solve detection, enumeration and segmentation from image-level annotations
Anthony Bilodeau
Constantin V. L. Delmas
M. Parent
Paul De Koninck
Audrey Durand
Flavie Lavoie-Cardinal
2022-04-14
Nature Machine Intelligence (published)
doi.org
Predicting Visual Improvement After Macular Hole Surgery: A Combined Model Using Deep Learning and Clinical Features
Alexandre Lachance
Mathieu Godbout
Fares Antaki
Mélanie Hébert
Serge Bourgault
Mathieu Caissie
Éric Tourville
Audrey Durand
Ali Dirani
2022-04-06
Translational Vision Science & Technology (published)
doi.org