Thomas E. Doyle
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View article: Between likes and lies: how teenage girls navigate online health information
Between likes and lies: how teenage girls navigate online health information Open
Introduction This study examined how high school teenage girls in the United States navigate and evaluate health information on social media, as well as the barriers they face in accessing and interpreting such content. Methods We conducte…
View article: Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research
Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research Open
This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world's population. The identified gaps and future research directions provide…
View article: Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques
Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques Open
Background The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet mental health…
View article: Who are the instructional assistant interns?: Examining the synergy of teaching assistants in first-year engineering course during the pandemic
Who are the instructional assistant interns?: Examining the synergy of teaching assistants in first-year engineering course during the pandemic Open
This work-in-progress paper will examine the synergy of roles of instructional assistant interns (IAIs) and teaching assistants (TAs) in remote teaching and learning of the redesigned first-year engineering curriculum. Unique to this redes…
View article: Data-driven approach to quantify trust in medical devices using Bayesian networks
Data-driven approach to quantify trust in medical devices using Bayesian networks Open
Bayesian networks are increasingly used to quantify the uncertainty of subjective and stochastic concepts such as trust. In this article, we propose a data-driven approach to estimate Bayesian parameters in the domain of wearable medical d…
View article: Prediction of Emergency Department Revisits among Child and Youth Mental Health Outpatients Using Deep Learning Techniques
Prediction of Emergency Department Revisits among Child and Youth Mental Health Outpatients Using Deep Learning Techniques Open
Background The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet mental health…
View article: Integrating intention-based systems in human-robot interaction: a scoping review of sensors, algorithms, and trust
Integrating intention-based systems in human-robot interaction: a scoping review of sensors, algorithms, and trust Open
The increasing adoption of robot systems in industrial settings and teaming with humans have led to a growing interest in human-robot interaction (HRI) research. While many robots use sensors to avoid harming humans, they cannot elaborate …
View article: XGBoost for Interpretable Alzheimer’s Decision Support
XGBoost for Interpretable Alzheimer’s Decision Support Open
Despite their necessity in directing patient care worldwide, simple and accurate diagnostic tools for early Alzheimer’s disease (AD) do not exist. To support healthcare decision-making and planning, this research leverages large, multi-sit…
View article: Method for enabling trust in collaborative research
Method for enabling trust in collaborative research Open
The presented application is a method for enabling verifiable trust in collaborative data sharing environments. The architecture supports the human-in-the-loop paradigm by establishing trust between participants, including human researcher…
View article: MLCM: Multi-Label Confusion Matrix
MLCM: Multi-Label Confusion Matrix Open
Concise and unambiguous assessment of a machine learning algorithm is key to classifier design and performance improvement. In the multi-class classification task, where each instance can only be labeled as one class, the confusion matrix …
View article: Evaluation methodology for deep learning imputation models
Evaluation methodology for deep learning imputation models Open
There is growing interest in imputing missing data in tabular datasets using deep learning. Existing deep learning–based imputation models have been commonly evaluated using root mean square error (RMSE) as the predictive accuracy metric. …
View article: Method for enabling trust in collaborative research
Method for enabling trust in collaborative research Open
The presented application is a method for enabling verifiable trust in collaborative data sharing environments. The architecture supports the human-in-the-loop paradigm by establishing trust between participants, including human researcher…
View article: Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systems
Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systems Open
The rapid integration of artificial intelligence across traditional research domains has generated an amalgamation of nomenclature. As cross-discipline teams work together on complex machine learning challenges, finding a consensus of basi…
View article: M4CVD: Mobile Machine Learning Model for Monitoring Cardiovascular Disease
M4CVD: Mobile Machine Learning Model for Monitoring Cardiovascular Disease Open
In this paper we present M4CVD: Mobile Machine Learning Model for Monitoring Cardiovascular Disease, a system designed specifically for mobile devices that facilitates monitoring of cardiovascular disease (CVD). The system uses wearable se…
View article: MLCM: Multi-Label Confusion Matrix
MLCM: Multi-Label Confusion Matrix Open
Concise and unambiguous assessment of a machine learning algorithm is key to classifier design and performance improvement. In the multi-class classification task, where each instance can only be labeled as one class, the confusion matrix …
View article: Evaluation methodology for deep learning imputation models
Evaluation methodology for deep learning imputation models Open
There is growing interest in imputing missing data in tabular datasets using deep learning. Existing deep learning–based imputation models have been commonly evaluated using root mean square error (RMSE) as the predictive accuracy metric. …
View article: Augmented Reality Based Brain Tumor 3D Visualization
Augmented Reality Based Brain Tumor 3D Visualization Open
In this paper we present an augmented reality system for mobile devices that facilitates 3D brain tumor visualization in real time. The system uses facial features to track the subject in the scene. The system performs camera calibration b…
View article: Augmented Reality Based Brain Tumor 3D Visualization
Augmented Reality Based Brain Tumor 3D Visualization Open
In this paper we present an augmented reality system for mobile devices that facilitates 3D brain tumor visualization in real time. The system uses facial features to track the subject in the scene. The system performs camera calibration b…
View article: From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes
From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes Open
There has been increased effort to understand the neurophysiological effects of concussion aimed to move diagnosis and identification beyond current subjective behavioral assessments that suffer from poor sensitivity. Recent evidence sugge…
View article: From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes
From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes Open
There has been increased effort to understand the neurophysiological effects of concussion aimed to move diagnosis and identification beyond current subjective behavioral assessments that suffer from poor sensitivity. Recent evidence sugge…
View article: Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systems
Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systems Open
The rapid integration of artificial intelligence across traditional research domains has generated an amalgamation of nomenclature. As cross-discipline teams work together on complex machine learning challenges, finding a consensus of basi…
View article: M4CVD: Mobile Machine Learning Model for Monitoring Cardiovascular Disease
M4CVD: Mobile Machine Learning Model for Monitoring Cardiovascular Disease Open
In this paper we present M4CVD: Mobile Machine Learning Model for Monitoring Cardiovascular Disease, a system designed specifically for mobile devices that facilitates monitoring of cardiovascular disease (CVD). The system uses wearable se…
View article: rWISDM: Repaired WISDM, a Public Dataset for Human Activity Recognition
rWISDM: Repaired WISDM, a Public Dataset for Human Activity Recognition Open
Human Activity Recognition (HAR) has become a spotlight in recent scientific research because of its applications in various domains such as healthcare, athletic competitions, smart cities, and smart home. While researchers focus on the me…
View article: Check It Before You Wreck It: A Guide to STAR-ML for Screening Machine Learning Reporting in Research
Check It Before You Wreck It: A Guide to STAR-ML for Screening Machine Learning Reporting in Research Open
Machine learning (ML) is a technique that learns to detect patterns and trends in data. However, the quality of reporting ML in research is often suboptimal, leading to inaccurate conclusions and hindering progress in the field, especially…
View article: COVID-19 international experience in paediatric patients with congenital heart disease
COVID-19 international experience in paediatric patients with congenital heart disease Open
Objective As COVID-19 continues to affect the global population, it is crucial to study the impact of the disease in vulnerable populations. This study of a diverse, international cohort aims to provide timely, experiential data on the cou…
View article: Integration of Core First Year Engineering Courses into Sequenced Experiential Learning: The Integrated Cornerstone
Integration of Core First Year Engineering Courses into Sequenced Experiential Learning: The Integrated Cornerstone Open
Until the beginning of the 2020 academic year, the first-year engineering program at McMaster University was organized as traditional courses to form a common curriculum for all students. The first year core courses were organized as i) De…
View article: Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systems
Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systems Open
The rapid integration of artificial intelligence across traditional research domains has generated an amalgamation of nomenclature. As cross-discipline teams work together on complex machine learning challenges, finding a consensus of basi…
View article: Evaluation methodology for deep learning imputation models
Evaluation methodology for deep learning imputation models Open
There is growing interest in imputing missing data in tabular datasets using deep learning. Existing deep learning–based imputation models have been commonly evaluated using root mean square error (RMSE) as the predictive accuracy metric. …
View article: Crew Autonomy During Simulated Medical Event Management on Long Duration Space Exploration Missions
Crew Autonomy During Simulated Medical Event Management on Long Duration Space Exploration Missions Open
Objective Our primary aim was to investigate crew performance during medical emergencies with and without ground-support from a flight surgeon located at mission control. Background There are gaps in knowledge regarding the potential for u…
View article: MLCM: Multi-Label Confusion Matrix
MLCM: Multi-Label Confusion Matrix Open
Concise and unambiguous assessment of a machine learning algorithm is key to classifier design and performance improvement. In the multi-class classification task, where each instance can only be labeled as one class, the confusion matrix …