Portrait de Audrey Durand

Audrey Durand

Membre académique associé
Chaire en IA Canada-CIFAR
Professeure adjointe, Université Laval, Département d'informatique et de génie logiciel
Sujets de recherche
Apprentissage en ligne
Apprentissage par renforcement
IA pour la science
Site web
Google Scholar
LinkedIn

Biographie

Audrey Durand est professeure adjointe au Département d’informatique et de génie logiciel ainsi qu’au Département de génie électrique et de génie informatique de l’Université Laval. Elle se spécialise dans les algorithmes qui apprennent par l’interaction avec leur environnement, soit l’apprentissage par renforcement, et s’intéresse particulièrement à l’application de ces approches au domaine de la santé.

Étudiants actuels

Julien Armand
Maîtrise recherche - Université Laval
Yohaï-Eliel Berreby
Doctorat - McGill
Site web
Github
Google Scholar
Baptiste Bonin
Maîtrise recherche - Université Laval
Hugo Delhaye
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Lune Bellec
Maxime Heuillet
Doctorat - Université Laval
Bruno Joyal
Maîtrise recherche - Université Laval
Alexandre Larouche
Doctorat - Université Laval
Github
Google Scholar
Randy Lefebvre
Doctorat - Université Laval
Hadi Moazen
Doctorat - Université Laval
Caro Strickland
Postdoctorat - Université Laval
Site web
Github
Google Scholar

Publications

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 … (voir plus)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.
2023-02-18
ArXiv (prépublication)
doi.org
arxiv.org
Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy
Alexandre Larouche
Audrey Durand
Richard Khoury
Caroline Sirois
2022-12-09
ArXiv (prépublication)
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
The banks of items of mobility domains represent a first step toward establishing a comprehensive outcome set and a common language of mobil… (voir plus)ity to develop the ontology. It enables researchers and healthcare professionals to begin exposing the content of mobility measures as a way to assess mobility comprehensively.
2022-12-08
BMC Neurology (publié)
doi.org
Performative Prediction in Time Series: A Case Study
Rupali Bhati
Jennifer Jones
David Langelier
Anthony Reiman
Jonathan Greenland
Kristin Campbell
Audrey Durand
2022-12-01
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
2022-06-27
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
Contextual bandit optimization of super-resolution microscopy
Anthony Bilodeau
Renaud Bernatchez
Albert Michaud-Gagnon
Flavie Lavoie-Cardinal
Audrey Durand
2022-05-26
Applied Informatics (publié)
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
2022-04-13
Nature Machine Intelligence (publié)
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
2022-04-05
Translational Vision Science & Technology (publié)
doi.org
Biomedical Research & Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Rehabilitation: Protocol for a longitudinal evaluation of mobility outcomes (Preprint)
Sara Ahmed
Philippe Archambault
Claudine Auger
Audrey Durand
Joyce Fung
Eva Kehayia
Anouk Lamontagne
Annette Majnemer
Sylvie Nadeau
Joelle Pineau
Alain Ptito
Bonnie Swaine
2022-01-12
(publié)
doi.org
GrowSpace: Learning How to Shape Plants
Yasmeen Hitti
Ionelia Buzatu
Manuel Del Verme
Mark Lefsrud
Florian Golemo
Audrey Durand
Plants are dynamic systems that are integral to our existence and survival. Plants face environment changes and adapt over time to their sur… (voir plus)rounding conditions. We argue that plant responses to an environmental stimulus are a good example of a real-world problem that can be approached within a reinforcement learning (RL)framework. With the objective of controlling a plant by moving the light source, we propose GrowSpace, as a new RL benchmark. The back-end of the simulator is implemented using the Space Colonisation Algorithm, a plant growing model based on competition for space. Compared to video game RL environments, this simulator addresses a real-world problem and serves as a test bed to visualize plant growth and movement in a faster way than physical experiments. GrowSpace is composed of a suite of challenges that tackle several problems such as control, multi-stage learning,fairness and multi-objective learning. We provide agent baselines alongside case studies to demonstrate the difficulty of the proposed benchmark.
2021-12-31
AAAI.org/2022/Workshop/AIAFS (publié)
openreview.net
openreview.net
Pharmacists' perceptions of a machine learning model for the identification of atypical medication orders
Sophie-Camille Hogue
Flora Chen
Geneviève Brassard
Denis Lebel
Jean-François Bussières
Audrey Durand
Maxime Thibault
2021-05-05
J. Am. Medical Informatics Assoc. (publié)
doi.org
Routine Bandits: Minimizing Regret on Recurring Problems
Hassan Saber
L'eo Saci
Odalric-Ambrym Maillard
Audrey Durand
2020-12-31
ECML/PKDD (publié)
doi.org