Publications

Artificial intelligence in nursing: Priorities and opportunities from an international invitational think‐tank of the Nursing and Artificial Intelligence Leadership Collaborative
Charlene Esteban Ronquillo
Laura‐Maria Peltonen
Lisiane Pruinelli
Charlene H Chu
Suzanne Bakken
Ana Beduschi
Kenrick Cato
Nicholas Hardiker
Alain Junger
Martin Michalowski
Rune Nyrup
Samira Abbasgholizadeh-Rahimi
Donald Nigel Reed
Tapio Salakoski
Sanna Salanterä
Nancy Walton
Patrick Weber
Thomas Wiegand
Maxim Topaz
2021-05-18
Journal of Advanced Nursing (published)
doi.org
Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems
Matt Grenander
Robert Belfer
Ekaterina Kochmar
Iulian V. Serban
Franccois St-Hilaire
Jackie Cheung
We explore creating automated, personalized feedback in an intelligent tutoring system (ITS). Our goal is to pinpoint correct and incorrect … (see more)concepts in student answers in order to achieve better student learning gains. Although automatic methods for providing personalized feedback exist, they do not explicitly inform students about which concepts in their answers are correct or incorrect. Our approach involves decomposing students answers using neural discourse segmentation and classification techniques. This decomposition yields a relational graph over all discourse units covered by the reference solutions and student answers. We use this inferred relational graph structure and a neural classifier to match student answers with reference solutions and generate personalized feedback. Although the process is completely automated and data-driven, the personalized feedback generated is highly contextual, domain-aware and effectively targets each student's misconceptions and knowledge gaps. We test our method in a dialogue-based ITS and demonstrate that our approach results in high-quality feedback and significantly improved student learning gains.
2021-05-18
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
DIBS: Diversity inducing Information Bottleneck in Model Ensembles
Samarth Sinha
Homanga Bharadhwaj
Anirudh Goyal
Hugo Larochelle
Animesh Garg
Florian Shkurti
2021-05-18
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Individual Fairness in Kidney Exchange Programs
Golnoosh Farnadi
William St-Arnaud
Behrouz Babaki
Margarida Carvalho
2021-05-18
AAAI Conference on Artificial Intelligence (published)
doi.org
Meta-learning framework with applications to zero-shot time-series forecasting
Boris Oreshkin
Dmitri Carpov
Nicolas Chapados
Yoshua Bengio
Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new… (see more) TS coming from different datasets? This work provides positive evidence to this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms. Our theoretical analysis suggests that residual connections act as a meta-learning adaptation mechanism, generating a subset of task-specific parameters based on a given TS input, thus gradually expanding the expressive power of the architecture on-the-fly. The same mechanism is shown via linearization analysis to have the interpretation of a sequential update of the final linear layer. Our empirical results on a wide range of data emphasize the importance of the identified meta-learning mechanisms for successful zero-shot univariate forecasting, suggesting that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining, resulting in performance that is at least as good as that of state-of-practice univariate forecasting models.
2021-05-18
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Metrics and continuity in reinforcement learning
Charline Le Lan
Marc Gendron-Bellemare
Pablo Samuel Castro
2021-05-18
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Self-Supervised Attention-Aware Reinforcement Learning
Haiping Wu
Khimya Khetarpal
Doina Precup
Visual saliency has emerged as a major visualization tool for interpreting deep reinforcement learning (RL) agents. However, much of the exi… (see more)sting research uses it as an analyzing tool rather than an inductive bias for policy learning. In this work, we use visual attention as an inductive bias for RL agents. We propose a novel self-supervised attention learning approach which can 1. learn to select regions of interest without explicit annotations, and 2. act as a plug for existing deep RL methods to improve the learning performance. We empirically show that the self-supervised attention-aware deep RL methods outperform the baselines in the context of both the rate of convergence and performance. Furthermore, the proposed self-supervised attention is not tied with specific policies, nor restricted to a specific scene. We posit that the proposed approach is a general self-supervised attention module for multi-task learning and transfer learning, and empirically validate the generalization ability of the proposed method. Finally, we show that our method learns meaningful object keypoints highlighting improvements both qualitatively and quantitatively.
2021-05-18
AAAI Conference on Artificial Intelligence (published)
doi.org
Variance Penalized On-Policy and Off-Policy Actor-Critic
Arushi Jain
Gandharv Patil
Ayush Jain
Khimya Khetarpal
Doina Precup
2021-05-18
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Imbalanced social-communicative and restricted repetitive behavior subtypes of autism spectrum disorder exhibit different neural circuitry
Natasha Bertelsen
Isotta Landi
Richard A.I. Bethlehem
Jakob Seidlitz
Elena Maria Busuoli
Veronica Mandelli
Eleonora Satta
Stavros Trakoshis
Bonnie Auyeung
Prantik Kundu
Eva Loth
Guillaume Dumas
Sarah Baumeister
Christian Beckmann
Sven Bölte
Thomas Bourgeron
Tony Charman
Sarah Durston
Christine Ecker
Rosemary Holt … (see 57 more)
Mark Johnson
Emily J. H. Jones
Luke Mason
Andreas Meyer-Lindenberg
Carolin Moessnang
Marianne Oldehinkel
Antonio Persico
Julian Tillmann
Steve C. R. Williams
Will Spooren
Declan Murphy
Jan K. Buitelaar
Jumana Sara Tobias Carsten Michael Daniel Claudia Yvette Bhismadev Chris Ineke Daisy Flavio Jessica Vincent Pilar David Lindsay Hannah Joerg Rosemary J. Xavier Liogier David J. René Andre Maarten Nico Bethany Laurence Bob Gahan Antonio M. Barbara Amber N. V. Jessica Roberto Antonia San José Emily Roberto Heike Jack Steve C. R. Caroline Marcel P. Ahmad
Simon Baron-Cohen
Jumana Ahmad
Meng-Chuan Lai
Sara Ambrosino
Michael V. Lombardo
Tobias Banaschewski
Carsten Bours
Michael Brammer
Daniel Brandeis
Claudia Brogna
Yvette de Bruijn
Bhismadev Chakrabarti
Christopher H. Chatham
Ineke Cornelissen
Daisy Crawley
Flavio Dell’Acqua
Jessica Faulkner
Vincent Frouin
Pilar Garcés
David Goyard
Lindsay Ham
Hannah Hayward
Joerg F. Hipp
Xavier Liogier D’ardhuy
David J. Lythgoe
René Mandl
Andre Marquand
Maarten Mennes
Nico Mueller
Beth Oakley
Laurence O’Dwyer
Bob Oranje
Gahan Pandina
Barbara Ruggeri
Amber N. V. Ruigrok
Jessica Sabet
Roberto Sacco
Antonia San José Cáceres
Emily Simonoff
Roberto Toro
Heike Tost
Jack Waldman
Caroline Wooldridge
Marcel P. Zwiers
2021-05-14
Communications Biology (published)
doi.org
Recurrent Traumatic Brain Injury Surveillance Using Administrative Health Data: A Bayesian Latent Class Analysis
Oliver Lasry
Nandini Dendukuri
Judith Marcoux
David Buckeridge
Background: The initial injury burden from incident TBI is significantly amplified by recurrent TBI (rTBI). Unfortunately, research assessin… (see more)g the accuracy to conduct rTBI surveillance is not available. Accurate surveillance information on recurrent injuries is needed to justify the allocation of resources to rTBI prevention and to conduct high quality epidemiological research on interventions that mitigate this injury burden. This study evaluates the accuracy of administrative health data (AHD) surveillance case definitions for rTBI and estimates the 1-year rTBI incidence adjusted for measurement error. Methods: A 25% random sample of AHD for Montreal residents from 2000 to 2014 was used in this study. Four widely used TBI surveillance case definitions, based on the International Classification of Disease and on radiological exams of the head, were applied to ascertain suspected rTBI cases. Bayesian latent class models were used to estimate the accuracy of each case definition and the 1-year rTBI measurement-error-adjusted incidence without relying on a gold standard rTBI definition that does not exist, across children (18 years), adults (18-64 years), and elderly (> =65 years). Results: The adjusted 1-year rTBI incidence was 4.48 (95% CrI 3.42, 6.20) per 100 person-years across all age groups, as opposed to a crude estimate of 8.03 (95% CrI 7.86, 8.21) per 100 person-years. Patients with higher severity index TBI had a significantly higher incidence of rTBI compared to patients with lower severity index TBI. The case definition that identified patients undergoing a radiological examination of the head in the context of any traumatic injury was the most sensitive across children [0.46 (95% CrI 0.33, 0.61)], adults [0.79 (95% CrI 0.64, 0.94)], and elderly [0.87 (95% CrI 0.78, 0.95)]. The most specific case definition was the discharge abstract database in children [0.99 (95% CrI 0.99, 1.00)], and emergency room visits claims in adults/elderly [0.99 (95% CrI 0.99, 0.99)]. Median time to rTBI was the shortest in adults (75 days) and the longest in children (120 days). Conclusion: Conducting accurate surveillance and valid epidemiological research for rTBI using AHD is feasible when measurement error is accounted for.
2021-05-14
Frontiers in Neurology (published)
doi.org
Common limitations of performance metrics in biomedical image analysis
Annika Reinke
Matthias Eisenmann
Minu Dietlinde Tizabi
Carole H. Sudre
TIM RÄDSCH
Michela Antonelli
Tal Arbel
Spyridon Bakas
M. Jorge Cardoso
Veronika Cheplygina
Keyvan Farahani
Ben Glocker
DOREEN HECKMANN-NÖTZEL
Fabian Isensee
Pierre Jannin
Charles Kahn
Jens Kleesiek
Tahsin Kurc
Michal Kozubek
Bennett Landman … (see 15 more)
GEERT LITJENS
Klaus Maier-Hein
Anne Lousise Martel
Bjoern Menze
Henning Müller
Jens Petersen
Mauricio Reyes
Nicola Rieke
Bram Stieltjes
Ronald M. Summers
Sotirios A. Tsaftaris
Bram van Ginneken
Annette Kopp-Schneider
Paul Jäger
Lena Maier-Hein
2021-05-11
MIDL.io/2021/Conference/Short (poster)
openreview.net
openreview.net
Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting
Brennan Nichyporuk
Justin Szeto
Douglas Arnold
Tal Arbel
There are many clinical contexts which require accurate detection and segmentation of all focal pathologies (e.g. lesions, tumours) in patie… (see more)nt images. In cases where there are a mix of small and large lesions, standard binary cross entropy loss will result in better segmentation of large lesions at the expense of missing small ones. Adjusting the operating point to accurately detect all lesions generally leads to oversegmentation of large lesions. In this work, we propose a novel reweighing strategy to eliminate this performance gap, increasing small pathology detection performance while maintaining segmentation accuracy. We show that our reweighing strategy vastly outperforms competing strategies based on experiments on a large scale, multi-scanner, multi-center dataset of Multiple Sclerosis patient images.
2021-05-11
MIDL.io/2021/Conference/Short (poster)
openreview.net
openreview.net