Publications

Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations

David Bergeron

Christian Iorio-Morin

Marco Bonizzato

Guillaume Lajoie

Nathalie Orr Gaucher

Éric Racine

Alexander G. Weil

2023-03-01

Journal of Child Neurology (published)

doi.org

Differentially Private Release of Heterogeneous Network for Managing Healthcare Data

Rashid Hussain Khokhar

Benjamin Fung

Farkhund Iqbal

Khalil Al-Hussaeni

Mohammed Hussain

With the increasing adoption of digital health platforms through mobile apps and online services, people have greater flexibility connecting… (see more) with medical practitioners, pharmacists, and laboratories and accessing resources to manage their own health-related concerns. Many healthcare institutions are connecting with each other to facilitate the exchange of healthcare data, with the goal of effective healthcare data management. The contents generated over these platforms are often shared with third parties for a variety of purposes. However, sharing healthcare data comes with the potential risk of exposing patients’ sensitive information to privacy threats. In this article, we address the challenge of sharing healthcare data while protecting patients’ privacy. We first model a complex healthcare dataset using a heterogeneous information network that consists of multi-type entities and their relationships. We then propose DiffHetNet, an edge-based differentially private algorithm, to protect the sensitive links of patients from inbound and outbound attacks in the heterogeneous health network. We evaluate the performance of our proposed method in terms of information utility and efficiency on different types of real-life datasets that can be modeled as networks. Experimental results suggest that DiffHetNet generally yields less information loss and is significantly more efficient in terms of runtime in comparison with existing network anonymization methods. Furthermore, DiffHetNet is scalable to large network datasets.

2023-02-28

ACM Transactions on Knowledge Discovery from Data (published)

doi.org

A case–control study on predicting population risk of suicide using health administrative data: a research protocol

JianLi Wang

Fatemeh Gholi Zadeh Kharrat

Jean-François Pelletier

Louis Rochette

Eric Pelletier

Pascale Lévesque

Victoria Massamba

Camille Brousseau-Paradis

Mada Mohammed

Geneviève Gariépy

Christian Gagné

Alain Lesage

2023-02-27

BMJ Open (published)

doi.org

DisKeyword: Tweet Corpora Exploration for Keyword Selection

Sacha Lévy

Reihaneh Rabbany

2023-02-27

Web Search and Data Mining (published)

doi.org

Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity

Xiaoxuan Yan

Ru Kong

Aihuiping Xue

Qing Yang

Csaba Orban

Lijun An

Avram J. Holmes

Xing Qian

Jianzhong Chen

Xi-Nian Zuo

Juan Helen Zhou

Marielle V Fortier

Ai Peng Tan

Peter Gluckman

Yap Seng Chong

Michael J Meaney

Danilo Bzdok

Simon B. Eickhoff

B.T. Thomas Yeo

2023-02-25

NeuroImage (published)

doi.org

Stochastic Simulated Quantum Annealing for Fast Solution of Combinatorial Optimization Problems

Naoya Onizawa

Ryoma Sasaki

Duckgyu Shin

Warren Gross

Takahiro Hanyu

In this paper, we introduce stochastic simulated quantum annealing (SSQA) for large-scale combinatorial optimization problems. SSQA is desig… (see more)ned based on stochastic computing and quantum Monte Carlo, which can simulate quantum annealing (QA) by using multiple replicas of spins (probabilistic bits) in classical computing. The use of stochastic computing leads to an efficient parallel spin-state update algorithm, enabling quick search for a solution around the global minimum energy. Therefore, SSQA realizes quantum-like annealing for large-scale problems and can handle fully connected models in combinatorial optimization, unlike QA. The proposed method is evaluated in MATLAB on graph isomorphism problems, which are typical combinatorial optimization problems. The proposed method achieves a convergence speed an order of magnitude faster than a conventional stochastic simulaated annealing method. Additionally, it can handle a 100-times larger problem size compared to QA and a 25-times larger problem size compared to a traditional SA method, respectively, for similar convergence probabilities.

2023-02-24

ArXiv (preprint)

arxiv.org

Commonality in Recommender Systems: Evaluating Recommender Systems to Enhance Cultural Citizenship

Andres Ferraro

Gustavo Ferreira

Fernando Diaz

Georgina Born

2023-02-22

ArXiv (preprint)

doi.org

arxiv.org

Recall, Robustness, and Lexicographic Evaluation

Fernando Diaz

Bhaskar Mitra

2023-02-22

ArXiv (preprint)

arxiv.org

Unsupervised Layer-wise Score Aggregation for Textual OOD Detection

Maxime Darrin

Guillaume Staerman

Eduardo Dadalto Câmara Gomes

Jackie Ck Cheung

Pablo Piantanida

Pierre Colombo

2023-02-20

ArXiv (preprint)

doi.org

arxiv.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

LAGrad: Statically Optimized Differentiable Programming in MLIR

Mai Jacob Peng

Christophe Dubach

2023-02-17

International Conference on Compiler Construction (published)

doi.org

Effects of incoming particle energy and cluster size on the G-value of hydrated electrons.

Alaina Bui