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Shayan Nejadshamsi

Postdoctorat - McGill
Superviseur⋅e principal⋅e
Samira Abbasgholizadeh-Rahimi
Sujets de recherche
Apprentissage de représentations
Apprentissage multimodal
Grands modèles de langage (LLM)
Modèles de diffusion
Modèles génératifs
Raisonnement
Réseaux de neurones en graphes
Théorie de l'apprentissage automatique
Vision par ordinateur
Google Scholar

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… (voir plus)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 (publié)
doi.org
Evaluation and improvement of algorithmic fairness for COVID-19 severity classification using Explainable Artificial Intelligence-based bias mitigation
Shayan Nejadshamsi
Charlene H. Chu
Katherine S. McGilton
Xiaoxiao Li
Charlene Ronquillo
Samira Abbasgholizadeh-Rahimi
The COVID-19 pandemic has highlighted the growing reliance on machine learning (ML) models for predicting disease severity, which is importa… (voir plus)nt for clinical decision-making and equitable resource allocation. While achieving high predictive accuracy is important, ensuring fairness in the prediction output of these models is equally important to prevent bias-driven disparities in healthcare. This study evaluates fairness in a machine learning-based COVID-19 severity classification model and proposes an Explainable AI (XAI)-based bias mitigation strategy to address sex-related bias. Using data from the Quebec Biobank, we developed an XGBoost-based multi-class classification model. Fairness was assessed using Subset Accuracy Parity Difference (SAPD) and Label-wise Equal Opportunity Difference (LEOD) metrics. Four bias mitigation strategies were implemented and evaluated: Fair Representation Learning, Fair Classifier Using Constraints, Adversarial Debiasing, and our proposed XAI-based method utilizing SHapley Additive exPlanations (SHAP) method for feature importance analysis. The study cohort included 1642 COVID-19 positive older adults (mean age: 77.5), balance equally between males and females. The baseline (unmitigated) classification model achieved 90.68% accuracy but exhibited a 10.11% Subset Accuracy Parity Difference between sexes, indicating a relatively large bias. The introduced XAI-based method demonstrated a better trade-off between model performance and fairness compared to existing bias mitigation methods by identifying sex-sensitive feature interactions and integrating them into the model re-training. Traditional fairness interventions often compromise accuracy to a greater extent. Our XAI-based method achieves the best balance between classification performance and bias, enhancing its clinical applicability. The XAI-driven bias mitigation intervention effectively reduces sex-based disparities in COVID-19 severity prediction without the significant accuracy loss observed in traditional methods. This approach provides a framework for developing fair and accurate clinical decision support systems for older adults, which ensures equitable care in clinical risk stratification and resource allocation.
2025-12-03
JAMIA Open (publié)
doi.org
Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study
Shayan Nejadshamsi
Vania Karami
Negar Ghourchian
Narges Armanfard
Howard Bergman
Roland Grad
Machelle Wilchesky
Vladimir Khanassov
Isabelle Vedel
Vania Karami
Depression, characterized by persistent sadness and loss of interest in daily activities, greatly reduces quality of life. Early detection i… (voir plus)s vital for effective treatment and intervention. While many studies use wearable devices to classify depression based on physical activity, these often rely on intrusive methods. Additionally, most depression classification studies involve large participant groups and use single-stage classifiers without explainability. This study aims to assess the feasibility of classifying depression using nonintrusive Wi-Fi–based motion sensor data using a novel machine learning model on a limited number of participants. We also conduct an explainability analysis to interpret the model’s predictions and identify key features associated with depression classification. In this study, we recruited adults aged 65 years and older through web-based and in-person methods, supported by a McGill University health care facility directory. Participants provided consent, and we collected 6 months of activity and sleep data via nonintrusive Wi-Fi–based sensors, along with Edmonton Frailty Scale and Geriatric Depression Scale data. For depression classification, we proposed a HOPE (Home-Based Older Adults’ Depression Prediction) machine learning model with feature selection, dimensionality reduction, and classification stages, evaluating various model combinations using accuracy, sensitivity, precision, and F1-score. Shapely addictive explanations and local interpretable model-agnostic explanations were used to explain the model’s predictions. A total of 6 participants were enrolled in this study; however, 2 participants withdrew later due to internet connectivity issues. Among the 4 remaining participants, 3 participants were classified as not having depression, while 1 participant was identified as having depression. The most accurate classification model, which combined sequential forward selection for feature selection, principal component analysis for dimensionality reduction, and a decision tree for classification, achieved an accuracy of 87.5%, sensitivity of 90%, and precision of 88.3%, effectively distinguishing individuals with and those without depression. The explainability analysis revealed that the most influential features in depression classification, in order of importance, were “average sleep duration,” “total number of sleep interruptions,” “percentage of nights with sleep interruptions,” “average duration of sleep interruptions,” and “Edmonton Frailty Scale.” The findings from this preliminary study demonstrate the feasibility of using Wi-Fi–based motion sensors for depression classification and highlight the effectiveness of our proposed HOPE machine learning model, even with a small sample size. These results suggest the potential for further research with a larger cohort for more comprehensive validation. Additionally, the nonintrusive data collection method and model architecture proposed in this study offer promising applications in remote health monitoring, particularly for older adults who may face challenges in using wearable devices. Furthermore, the importance of sleep patterns identified in our explainability analysis aligns with findings from previous research, emphasizing the need for more in-depth studies on the role of sleep in mental health, as suggested in the explainable machine learning study.
2025-03-02
JMIR Aging (publié)
doi.org