Publications

259 Development of a Cost-Efficient Scintillation-Fiber Detector for Use in Automated Synthesis of Positron Emission Tomography Radiotracers
Hailey Ahn
Liam Carroll
Robert Hopewell
I-Huang Tsai
S. Enger
2023-08-31
Radiotherapy and Oncology (published)
doi.org
Impact of a vaccine passport on first-dose COVID-19 vaccine coverage by age and area-level social determinants in the Canadian provinces of Québec and Ontario: an interrupted time series analysis
Jorge Luis Flores Anato
Huiting Ma
Mackenzie A. Hamilton
Yiqing Xia
Sam Harper
David Buckeridge
Marc Brisson
Michael P. Hillmer
Kamil Malikov
Aidin Kerem
Reed Beall
Caroline E Wagner
Étienne Racine
Stefan Baral
Ève Dubé
Sharmistha Mishra
Mathieu Maheu-Giroux
In Canada, all provinces implemented vaccine passports in 2021 to increase vaccine uptake and reduce transmission in non-essential indoor sp… (see more)aces. We evaluate the impact of vaccine passport policies on first-dose COVID-19 vaccination coverage by age, area-level income and proportion racialized. We performed interrupted time-series analyses using vaccine registry data linked to census information in Québec and Ontario (20.5 million people ≥12 years; unit of analysis: dissemination area). We fit negative binomial regressions to weekly first-dose vaccination, using a natural spline to capture pre-announcement trends, adjusting for baseline vaccination coverage (start: July 3 rd ; end: October 23 rd Québec, November 13 th Ontario). We obtain counterfactual vaccination rates and coverage, and estimated vaccine passports’ impact on vaccination coverage (absolute) and new vaccinations (relative). In both provinces, pre-announcement first-dose vaccination coverage was 82% (≥12 years). The announcement resulted in estimated increases in vaccination coverage of 0.9 percentage points (p.p.;95%CI:0.4-1.2) in Québec and 0.7 p.p. (95%CI:0.5-0.8) in Ontario. In relative terms, these increases correspond to 23% (95%CI:10-36%) and 19% (95%CI:15-22%) more vaccinations. The impact was larger among people aged 12-39 (1-2 p.p.). There was little variability in the absolute impact by area-level income or proportion racialized in either province. In the context of high baseline vaccine coverage across two provinces, the announcement of vaccine passports led to a small impact on first-dose coverage, with little impact on reducing economic and racial inequities in vaccine coverage. Findings suggest the need for other policies to further increase vaccination coverage among lower-income and more racialized neighbourhoods and communities. Vaccine passport policies increased COVID-19 vaccination coverage by approximately 1 percentage point (19 to 23% increase in vaccinations) in Québec and Ontario, Canada. Although vaccine passport policies increased vaccination coverage, absolute gains were limited in the context of high prior vaccine coverage. Vaccine passports had little impact on reducing economic and racial inequities in vaccine coverage.
2023-08-31
CMAJ Open (published)
doi.org
Integrating equity, diversity and inclusion throughout the lifecycle of AI within healthcare: a scoping review protocol
Milka Nyariro
Elham Emami
Pascale Caidor
S. A. Rahimi
2023-08-31
BMJ Open (published)
doi.org
Online Bayesian Optimization of Nerve Stimulation
Lorenz Wernisch
Tristan Edwards
Antonin Berthon
Olivier Tessier-Larivière
Elvijs Sarkans
Myrta Stoukidi
Pascal Fortier-Poisson
Max Pinkney
Michael Thornton
Catherine Hanley
Susannah Lee
Joel Jennings
Ben Appleton
Phillip Garsed
Bret Patterson
Buttinger Will
Samuel Gonshaw
Matjaž Jakopec
Sudhakaran Shunmugam
Jorin Mamen … (see 4 more)
Aleksi Tukiainen
Guillaume Lajoie
Oliver Armitage
Emil Hewage
In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs modifying their function. Stimulation devi… (see more)ces, capable of triggering exogenous neural signals using electrical wave forms, require a complex and multi-dimensional parameter space in order to control such wave forms. Determining the best combination of parameters (wave form optimization, or dosing) for treating a particular patient’s illness is therefore challenging. Comprehensive parameter searching for an optimal stimulation effect is often infeasible in a clinical setting, due to the size of the parameter space. Restricting this space, however, may lead to sub-optimal therapeutic results, reduced responder rates, and adverse effects. As an alternative to a full parameter search, we present a flexible machine learning, data acquisition and processing framework for optimizing neural stimulation parameters requiring as few steps as possible using Bayesian optimization. Such optimization builds a model of the neural and physiological responses to stimulations enabling it to optimize stimulation parameters and to provide estimates of the accuracy of the response model. The vagus nerve innervates, among other thoracic and visceral organs, the heart, thus controlling heart rate and is therefore ideal for demonstrating the effectiveness of our approach. Main results. The efficacy of our optimization approach was first evaluated on simulated neural responses, then applied to vagus nerve stimulation intraoperatively in porcine subjects. Optimization converged quickly on parameters achieving target heart rates and optimizing neural B-fibre activations despite high intersubject variability. An optimized stimulation waveform was achieved in real time with far fewer stimulations than required by alternative optimization strategies, thus minimizing exposure to side effects. Uncertainty estimates helped avoiding stimulations outside a safe range. Our approach shows that a complex set of neural stimulation parameters can be optimized in real-time for a patient to achieve a personalized precision dosing.
2023-08-31
bioRxiv (preprint)
doi.org
PP02  Presentation Time: 4:39 PM
Maryam Rahbaran
Jonathan Kalinowski
James Man Git Tsui
Joseph DeCunha
Kevin Croce
Brian Bergmark
Philip Devlin
S. Enger
2023-08-31
Brachytherapy (published)
doi.org
Saturday, June 24, 20238:30 AM - 9:30 AMMSOR01 Presentation Time: 8:30 AM
Mélodie Cyr
N. Chabaytah
Joud Babik
Behnaz Behmand
S. Enger
2023-08-31
Brachytherapy (published)
doi.org
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series
Jean-christophe Gagnon-audet
Kartik Ahuja
Mohammad Javad Darvishi Bayazi
Pooneh Mousavi
Guillaume Dumas
Irina Rish
2023-08-31
TMLR (accepted)
openreview.net
openreview.net
Beyond the ML Model: Applying Safety Engineering Frameworks to Text-to-Image Development
Shalaleh Rismani
Renee Shelby
Andrew J Smart
Renelito Delos Santos
AJung Moon
Negar Rostamzadeh
Identifying potential social and ethical risks in emerging machine learning (ML) models and their applications remains challenging. In this … (see more)work, we applied two well-established safety engineering frameworks (FMEA, STPA) to a case study involving text-to-image models at three stages of the ML product development pipeline: data processing, integration of a T2I model with other models, and use. Results of our analysis demonstrate the safety frameworks – both of which are not designed explicitly examine social and ethical risks – can uncover failure and hazards that pose social and ethical risks. We discovered a broad range of failures and hazards (i.e., functional, social, and ethical) by analyzing interactions (i.e., between different ML models in the product, between the ML product and user, and between development teams) and processes (i.e., preparation of training data or workflows for using an ML service/product). Our findings underscore the value and importance of examining beyond an ML model in examining social and ethical risks, especially when we have minimal information about an ML model.
2023-08-28
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (published)
doi.org
arxiv.org
Policy composition in reinforcement learning via multi-objective policy optimization
Shruti Mishra
Ankit Anand
Jordan Hoffmann
Nicolas Heess
Martin A. Riedmiller
Abbas Abdolmaleki
Doina Precup
We enable reinforcement learning agents to learn successful behavior policies by utilizing relevant pre-existing teacher policies. The teach… (see more)er policies are introduced as objectives, in addition to the task objective, in a multi-objective policy optimization setting. Using the Multi-Objective Maximum a Posteriori Policy Optimization algorithm (Abdolmaleki et al. 2020), we show that teacher policies can help speed up learning, particularly in the absence of shaping rewards. In two domains with continuous observation and action spaces, our agents successfully compose teacher policies in sequence and in parallel, and are also able to further extend the policies of the teachers in order to solve the task. Depending on the specified combination of task and teacher(s), teacher(s) may naturally act to limit the final performance of an agent. The extent to which agents are required to adhere to teacher policies are determined by hyperparameters which determine both the effect of teachers on learning speed and the eventual performance of the agent on the task. In the humanoid domain (Tassa et al. 2018), we also equip agents with the ability to control the selection of teachers. With this ability, agents are able to meaningfully compose from the teacher policies to achieve a superior task reward on the walk task than in cases without access to the teacher policies. We show the resemblance of composed task policies with the corresponding teacher policies through videos.
2023-08-28
ArXiv (preprint)
doi.org
arxiv.org
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
Renee Shelby
Shalaleh Rismani
Kathryn Henne
AJung Moon
Negar Rostamzadeh
Paul Nicholas
N'Mah Yilla-Akbari
Jess Gallegos
Andrew J Smart
Emilio Garcia
Gurleen Virk
2023-08-28
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (published)
doi.org
arxiv.org
What does it mean to be a responsible AI practitioner: An ontology of roles and skills
Shalaleh Rismani
AJung Moon
With the growing need to regulate AI systems across a wide variety of application domains, a new set of occupations has emerged in the indus… (see more)try. The so-called responsible Artificial Intelligence (AI) practitioners or AI ethicists are generally tasked with interpreting and operationalizing best practices for ethical and safe design of AI systems. Due to the nascent nature of these roles, however, it is unclear to future employers and aspiring AI ethicists what specific function these roles serve and what skills are necessary to serve the functions. Without clarity on these, we cannot train future AI ethicists with meaningful learning objectives. In this work, we examine what responsible AI practitioners do in the industry and what skills they employ on the job. We propose an ontology of existing roles alongside skills and competencies that serve each role. We created this ontology by examining the job postings for such roles over a two-year period (2020-2022) and conducting expert interviews with fourteen individuals who currently hold such a role in the industry. Our ontology contributes to business leaders looking to build responsible AI teams and provides educators with a set of competencies that an AI ethics curriculum can prioritize.
2023-08-28
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (published)
doi.org
arxiv.org
Beyond performance: the role of task demand, effort, and individual differences in ab initio pilots
Mohammad-Javad Darvishi-Bayazi
Andrew Law
Sergio Mejia Romero
Sion Jennings
Irina Rish
Jocelyn Faubert
Aviation safety depends on the skill and expertise of pilots to meet the task demands of flying an aircraft in an effective and efficient ma… (see more)nner. During flight training, students may respond differently to imposed task demands based on individual differences in capacity, physiological arousal, and effort. To ensure that pilots achieve a common desired level of expertise, training programs should account for individual differences to optimize pilot performance. This study investigates the relationship between task performance and physiological correlates of effort in ab initio pilots. Twenty-four participants conducted a flight simulator task with three difficulty levels and were asked to rate their perceived demand and effort using the NASA TLX. We recorded heart rate, EEG brain activity, and pupil size to assess changes in the participants’ mental and physiological states across different task demands. We found that, despite group-level correlations between performance error and physiological responses, individual differences in physiological responses to task demands reflected different levels of participant effort and task efficiency. These findings suggest that physiological monitoring of student pilots might provide beneficial insights to flight instructors to optimize pilot training at the individual level.
2023-08-27
Scientific Reports (published)
doi.org