Audrey Durand

A. Dirani

Purpose The purpose of this study was to assess the feasibility of deep learning (DL) methods to enhance the prediction of visual acuity (VA… (voir plus)) improvement after macular hole (MH) surgery from a combined model using DL on high-definition optical coherence tomography (HD-OCT) B-scans and clinical features. Methods We trained a DL convolutional neural network (CNN) using pre-operative HD-OCT B-scans of the macula and combined with a logistic regression model of pre-operative clinical features to predict VA increase ≥15 Early Treatment Diabetic Retinopathy Study (ETDRS) letters at 6 months post-vitrectomy in closed MHs. A total of 121 MHs with 242 HD-OCT B-scans and 484 clinical data points were used to train, validate, and test the model. Prediction of VA increase was evaluated using the area under the receiver operating characteristic curve (AUROC) and F1 scores. We also extracted the weight of each input feature in the hybrid model. Results All performances are reported on the held-out test set, matching results obtained with cross-validation. Using a regression on clinical features, the AUROC was 80.6, with an F1 score of 79.7. For the CNN, relying solely on the HD-OCT B-scans, the AUROC was 72.8 ± 14.6, with an F1 score of 61.5 ± 23.7. For our hybrid regression model using clinical features and CNN prediction, the AUROC was 81.9 ± 5.2, with an F1 score of 80.4 ± 7.7. In the hybrid model, the baseline VA was the most important feature (weight = 59.1 ± 6.9%), while the weight of HD-OCT prediction was 9.6 ± 4.2%. Conclusions Both the clinical data and HD-OCT models can predict postoperative VA improvement in patients undergoing vitrectomy for a MH with good discriminative performances. Combining them into a hybrid model did not significantly improve performance. Translational Relevance OCT-based DL models can predict postoperative VA improvement following vitrectomy for MH but fusing those models with clinical data might not provide improved predictive performance.

2022-04-01

Translational Vision Science & Technology (published)

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 (Preprint)

Sara Ahmed

Philippe Archambault

Claudine Auger

Joyce Fung

Eva Kehayia

Anouk Lamontagne

Annette Majnemer

Sylvie Nadeau

Alain Ptito

Bonnie Swaine

2022-01-13

(publié)

Sara Ahmed

P. Archambault

Claudine Auger

Joyce Phua Pau Fung

Eva Kehayia

Anouk Lamontagne

Annette Majnemer

Sylvie Nadeau

Alain Ptito

B. Swaine

Background Rapid advances in technologies over the past 10 years have enabled large-scale biomedical and psychosocial rehabilitation researc… (voir plus)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

2022-01-13

JMIR Research Protocols (published)

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

Joyce Fung

Eva Kehayia

Anouk Lamontagne

Annette Majnemer

Sylvie Nadeau

Alain Ptito

Bonnie Swaine

UNSTRUCTURED The Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Re… (voir plus)habilitation (BRILLIANT) program to provide evidence-based research to improve rehabilitation for individuals with Acquired Brain Injury (ABI: traumatic brain injury [TBI], cerebral palsy-fetal/perinatal brain injury, and stroke). The vision of the BRILLIANT program is to optimize mobility of persons with ABI across the lifespan. 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 sub-groups. These innovations will be used by front-line clinicians to deliver cost-effective care; the right intervention to the right person at the right time, accounting for long-term functional potential and meaningful participation in the community.

2022-01-13

(published)

GrowSpace: Learning How to Shape Plants

Yasmeen Hitti

Ionelia Buzatu

Manuel Del Verme

Mark Lefsrud

Florian Golemo

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.

2022-01-01

AAAI.org/2022/Workshop/AIAFS (publié)

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

Maxime Thibault

2021-05-06

J. Am. Medical Informatics Assoc. (publié)

Routine Bandits: Minimizing Regret on Recurring Problems

Hassan Saber

L'eo Saci

Odalric-Ambrym Maillard

2021-01-01

ECML/PKDD (publié)

Deep interpretability for GWAS

Deepak Sharma

Marc-andr'e Legault

Louis-philippe Lemieux Perreault

Audrey Lemaccon

Marie-Pierre Dub'e

Genome-Wide Association Studies are typically conducted using linear models to find genetic variants associated with common diseases. In the… (voir plus)se studies, association testing is done on a variant-by-variant basis, possibly missing out on non-linear interaction effects between variants. Deep networks can be used to model these interactions, but they are difficult to train and interpret on large genetic datasets. We propose a method that uses the gradient based deep interpretability technique named DeepLIFT to show that known diabetes genetic risk factors can be identified using deep models along with possibly novel associations.

2020-07-03

ArXiv (prépublication)

arxiv.org

Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks

Maxime Wabartha

Vincent Francois-Lavet

By virtue of their expressive power, neural networks (NNs) are well suited to fitting large, complex datasets, yet they are also known to … (voir plus)produce similar predictions for points outside the training distribution. As such, they are, like humans, under the influence of the Black Swan theory: models tend to be extremely "surprised" by rare events, leading to potentially disastrous consequences, while justifying these same events in hindsight. To avoid this pitfall, we introduce DENN, an ensemble approach building a set of Diversely Extrapolated Neural Networks that fits the training data and is able to generalize more diversely when extrapolating to novel data points. This leads DENN to output highly uncertain predictions for unexpected inputs. We achieve this by adding a diversity term in the loss function used to train the model, computed at specific inputs. We first illustrate the usefulness of the method on a low-dimensional regression problem. Then, we show how the loss can be adapted to tackle anomaly detection during classification, as well as safe imitation learning problems.

2020-07-01

International Joint Conference on Artificial Intelligence (publié)

Old Dog Learns New Tricks: Randomized UCB for Bandit Problems

Sharan Vaswani

Abbas Mehrabian

Branislav Kveton

2020-06-03

Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (publié)

proceedings.mlr.press

arxiv.org

Leveraging exploration in off-policy algorithms via normalizing flows

Bogdan Mazoure

Thang Doan

(Rex) Devon Hjelm

Exploration is a crucial component for discovering approximately optimal policies in most high-dimensional reinforcement learning (RL) setti… (voir plus)ngs with sparse rewards. Approaches such as neural density models and continuous exploration (e.g., Go-Explore) have been instrumental in recent advances. Soft actor-critic (SAC) is a method for improving exploration that aims to combine off-policy updates while maximizing the policy entropy. We extend SAC to a richer class of probability distributions through normalizing flows, which we show improves performance in exploration, sample complexity, and convergence. Finally, we show that not only the normalizing flow policy outperforms SAC on MuJoCo domains, it is also significantly lighter, using as low as 5.6% of the original network's parameters for similar performance.

2020-05-12

Proceedings of the Conference on Robot Learning (publié)

proceedings.mlr.press

arxiv.org

Literature Mining for Incorporating Inductive Bias in Biomedical Prediction Tasks (Student Abstract)

Qizhen Zhang

2020-04-03

AAAI Conference on Artificial Intelligence (publié)