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Ting Wang

PhD - McGill University
Supervisor
Samira Abbasgholizadeh-Rahimi
Research Topics
AI in Health
Explainable AI (XAI)
Responsible AI

Publications

Machine learning–based prediction of Metabolic Syndrome risk in the Quebec population
Haowei Qiu
Shayan Nejadshamsi
Ting Wang
Stella S. Daskalopoulou
Samira Abbasgholizadeh Rahimi
Objective This study evaluates multiple machine learning approaches to predict metabolic syndrome (MetS) risk in the Quebec, Canada populati… (see more)on. We further perform explainability analysis to interpret model predictions and identify key features driving risk classification. Methods and analysis This study followed the Minimum Information about Clinical Artificial Intelligence Modeling (MI-CLAIM) guideline for reporting. We used cross-sectional data from the Canadian Community Health Survey (2015–2018) for the population living in the province of Quebec, which includes 42,279 participants. Partial sampling was used to obtain a balanced dataset for model development. We evaluated seven machine learning models for the defined classification task, including Logistic Regression, XGBoost, LightGBM, TabNet, NODE, 1D-CNN and Regularisation Cocktails. Performance was assessed using accuracy, precision, recall, F1-score, AUROC, and AUPRC, and interpretability was examined using SHAP to identify key predictors of MetS risk. Results After partial sampling, 7,866 participants (4,856 high-risk and 3,010 low-risk MetS cases) were included in the machine learning analysis. XGBoost and NODE showed the strongest performance. XGBoost achieved the highest accuracy (80.4%) and AUROC (84.1%), while NODE achieved the highest precision (80.1%) and AUPRC (86.0%). Explainability analysis identified age, perceived health, and sex as the most important features contributing to MetS risk predictions. Conclusion This study shows that machine learning can accurately predict MetS risk using self-reported health survey data from the Quebec population. Comparison of classical and deep learning approaches identified the optimal predictive model, and explainability analyses identified the most important features contributing to the risk predictions, which align with established clinical evidence. These results support a machine learning–driven initial screening framework for population-level early identification of high-risk individuals, enabling targeted interventions and efficient allocation of healthcare resources.
2026-02-28
BMJ Digital Health & AI (published)
doi.org
Integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence for healthcare: a scoping review
Ting Wang
Elham Emami
Dana Jafarpour
Raymond Tolentino
Genevieve Gore
S. A. Rahimi
The lack of Equity, Diversity, and Inclusion (EDI) principles in the lifecycle of Artificial Intelligence (AI) technologies in healthcare is… (see more) a growing concern. Despite its importance, there is still a gap in understanding the initiatives undertaken to address this issue. This review aims to explore what and how EDI principles have been integrated into the design, development, and implementation of AI studies in healthcare. We followed the scoping review framework by Levac et al. and the Joanna Briggs Institute. A comprehensive search was conducted until April 29, 2022, across MEDLINE, Embase, PsycInfo, Scopus, and SCI-EXPANDED. Only research studies in which the integration of EDI in AI was the primary focus were included. Non-research articles were excluded. Two independent reviewers screened the abstracts and full texts, resolving disagreements by consensus or by consulting a third reviewer. To synthesize the findings, we conducted a thematic analysis and used a narrative description. We adhered to the PRISMA-ScR checklist for reporting scoping reviews. The search yielded 10,664 records, with 42 studies included. Most studies were conducted on the American population. Previous research has shown that AI models improve when socio-demographic factors such as gender and race are considered. Despite frameworks for EDI integration, no comprehensive approach systematically applies EDI principles in AI model development. Additionally, the integration of EDI into the AI implementation phase remains under-explored, and the representation of EDI within AI teams has been overlooked. This review reports on what and how EDI principles have been integrated into the design, development, and implementation of AI technologies in healthcare. We used a thorough search strategy and rigorous methodology, though we acknowledge limitations such as language and publication bias. A comprehensive framework is needed to ensure that EDI principles are considered throughout the AI lifecycle. Future research could focus on strategies to reduce algorithmic bias, assess the long-term impact of EDI integration, and explore policy implications to ensure that AI technologies are ethical, responsible, and beneficial for all.
2025-06-30
PLOS Digital Health (published)
doi.org