Publications

RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
Thibaud Godon
Pier-Luc Plante
Baptiste Bauvin
Élina Francovic-Fontaine
Alexandre Drouin
Jacques Corbeil
Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine. The high dimensio… (see more)nality of metabolomics data challenges learning algorithms in terms of scalability and generalization. Most learning algorithms do not produce interpretable models -- Method: We propose an ensemble learning algorithm based on conjunctions or disjunctions of decision rules. -- Results : Applications on metabolomics data shows that it produces models that achieves high predictive performances. The interpretability of the models makes them useful for biomarker discovery and patterns discovery in high dimensional data.
2022-08-11
ArXiv (preprint)
doi.org
arxiv.org
Learning to Improve Code Efficiency
Binghong Chen
Danny Tarlow
Kevin Swersky
Martin Maas
Pablo Heiber
Ashish V Naik
Milad Hashemi
Parthasarathy Ranganathan
2022-08-09
ArXiv (preprint)
doi.org
openreview.net
Estimating the lagged effect of price discounting: a time-series study on sugar sweetened beverage purchasing in a supermarket
Hiroshi Mamiya
Alexandra M. Schmidt
Erica E. M. Moodie
David Buckeridge
2022-08-06
BMC Public Health (published)
doi.org
Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers
Amar Kumar
Anjun Hu
Brennan Nichyporuk
Jean-Pierre R. Falet
Douglas Arnold
Sotirios A. Tsaftaris
Tal Arbel
2022-08-03
ArXiv (preprint)
doi.org
arxiv.org
Application of Artificial Intelligence in Shared Decision Making: Scoping Review
Samira Abbasgholizadeh-Rahimi
Michelle Cwintal
Yuhui Huang
Pooria Ghadiri
Roland Grad
Dan Poenaru
Genevieve Gore
Hervé Tchala Vignon Zomahoun
France Légaré
Pierre Pluye
Background Artificial intelligence (AI) has shown promising results in various fields of medicine. It has the potential to facilitate shared… (see more) decision making (SDM). However, there is no comprehensive mapping of how AI may be used for SDM. Objective We aimed to identify and evaluate published studies that have tested or implemented AI to facilitate SDM. Methods We performed a scoping review informed by the methodological framework proposed by Levac et al, modifications to the original Arksey and O'Malley framework of a scoping review, and the Joanna Briggs Institute scoping review framework. We reported our results based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) reporting guideline. At the identification stage, an information specialist performed a comprehensive search of 6 electronic databases from their inception to May 2021. The inclusion criteria were: all populations; all AI interventions that were used to facilitate SDM, and if the AI intervention was not used for the decision-making point in SDM, it was excluded; any outcome related to patients, health care providers, or health care systems; studies in any health care setting, only studies published in the English language, and all study types. Overall, 2 reviewers independently performed the study selection process and extracted data. Any disagreements were resolved by a third reviewer. A descriptive analysis was performed. Results The search process yielded 1445 records. After removing duplicates, 894 documents were screened, and 6 peer-reviewed publications met our inclusion criteria. Overall, 2 of them were conducted in North America, 2 in Europe, 1 in Australia, and 1 in Asia. Most articles were published after 2017. Overall, 3 articles focused on primary care, and 3 articles focused on secondary care. All studies used machine learning methods. Moreover, 3 articles included health care providers in the validation stage of the AI intervention, and 1 article included both health care providers and patients in clinical validation, but none of the articles included health care providers or patients in the design and development of the AI intervention. All used AI to support SDM by providing clinical recommendations or predictions. Conclusions Evidence of the use of AI in SDM is in its infancy. We found AI supporting SDM in similar ways across the included articles. We observed a lack of emphasis on patients’ values and preferences, as well as poor reporting of AI interventions, resulting in a lack of clarity about different aspects. Little effort was made to address the topics of explainability of AI interventions and to include end-users in the design and development of the interventions. Further efforts are required to strengthen and standardize the use of AI in different steps of SDM and to evaluate its impact on various decisions, populations, and settings.
2022-08-01
JMIR Medical Informatics (published)
doi.org
Endorsing Complexity Through Diversity: Computational Psychiatry Meets Big Data Analytics
Jakub Kopal
Danilo Bzdok
2022-08-01
Biological Psychiatry (published)
doi.org
Induced pluripotent stem cells display a distinct set of MHC I-associated peptides shared by human cancers
Anca Apavaloaei
Leslie Hesnard
Marie-Pierre Hardy
Basma Benabdallah
Grégory Ehx
Catherine Thériault
Jean-Philippe Laverdure
Chantal Durette
Joël Lanoix
Mathieu Courcelles
Nandita Noronha
Kapil Dev Chauhan
Sébastien Lemieux
Christian Beauséjour
Mick Bhatia
Pierre Thibault
Claude Perreault
2022-08-01
Cell Reports (published)
doi.org
Information Gain Sampling for Active Learning in Medical Image Classification
Raghav Mehta
Changjian Shui
Brennan Nichyporuk
Tal Arbel
2022-08-01
ArXiv (preprint)
doi.org
arxiv.org
A portrait of the different configurations between digitally-enabled innovations and climate governance
Pierre J. C. Chuard
Jennifer Garard
Karsten A. Schulz
Nilushi Kumarasinghe
David Rolnick
Damon Matthews
2022-08-01
Earth System Governance (published)
doi.org
The generalizability of pre-processing techniques on the accuracy and fairness of data-driven building models: a case study
Ying Sun
Benjamin Fung
Fariborz Haghighat
2022-08-01
Energy and Buildings (published)
doi.org
Single‐pass stratified importance resampling
Ege Ciklabakkal
Adrien Gruson
Iliyan Georgiev
Derek Nowrouzezahrai
Toshiya Hachisuka
Resampling is the process of selecting from a set of candidate samples to achieve a distribution (approximately) proportional to a desired t… (see more)arget. Recent work has revisited its application to Monte Carlo integration, yielding powerful and practical importance sampling methods. One drawback of existing resampling methods is that they cannot generate stratified samples. We propose two complementary techniques to achieve efficient stratified resampling. We first introduce bidirectional CDF sampling which yields the same result as conventional inverse CDF sampling but in a single pass over the candidates, without needing to store them, similarly to reservoir sampling. We then order the candidates along a space‐filling curve to ensure that stratified CDF sampling of candidate indices yields stratified samples in the integration domain. We showcase our method on various resampling‐based rendering problems.
2022-07-30
Computer Graphics Forum (published)
doi.org
BioCaster in 2021: automatic disease outbreaks detection from global news media
Zaiqiao Meng
Anya Okhmatovskaia
Maxime Polleri
Yannan Shen
Guido Powell
Zihao Fu
Iris Ganser
Meiru Zhang
Nicholas B King
David Buckeridge
Nigel Collier
2022-07-28
Bioinformatics (published)
doi.org