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Laya Rafiee Sevyeri

Alumni
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Publications

Artificial intelligence–driven prioritization of colonoscopy referrals using multicenter clinical data: addressing imbalanced and incomplete data
Laya Rafiee Sevyeri
Myriam Martel
Charles Ménard
Daniel von Renteln
Harminder Singh
Saro Aprikian
Dong Hyun Kim
Alan N. Barkun
Shirin Abbasinejad Enger
Long wait times for colonoscopy and challenges in referral prioritization contribute to procedural backlogs in endoscopy units, potentially … (voir plus)delaying the diagnosis of colorectal cancer (CRC) and other clinically significant conditions. In Québec, colonoscopy referrals are currently prioritized using structured clinical indications documented in the AH-702 referral form. This study aimed to develop and externally validate an explainable artificial intelligence (AI) framework for predicting colonoscopy referral priorities across multiple healthcare centers while addressing missing data and class imbalance. This multicenter retrospective study included 11,680 patients referred for colonoscopy at two tertiary centers in Québec, Canada, between 2018 and 2022. Using 135 clinical, demographic, diagnostic, and pathological features, a multiclass decision-tree model was trained with advanced imputation and synthetic data generation. The model was evaluated on an internal held-out test set and an independent external cohort of 2,977 patients. The optimized model achieved an area under the receiver operating characteristic curve (AUROC) of 98.4% and a balanced accuracy of 95.7% on the internal held-out test set. External evaluation yielded an AUROC of 97.9% and a balanced accuracy of 96.4%. When the 22 structured AH-702 indication fields were withheld, balanced accuracy decreased to 28.1% internally and 28.5% externally, while AUROC decreased to 60.1% and 61.3%, respectively. These findings demonstrate that the model identified clinically relevant referral indications as key predictors, consistent with the existing AH-702 prioritization framework. This interpretable AI framework demonstrated strong retrospective performance for predicting colonoscopy referral priorities across three healthcare centers. The findings support further investigation of its potential as a decision-support tool for referral prioritization, pending prospective validation.
2026-10-11
International Journal of Colorectal Disease (publié)
doi.org
Prognostic data extraction harnessing a privacy-preserving large language model: a clinician-AI collaborative retrospective evaluation in head and neck oncology
Yujing Zou
Laya Rafiee Sevyeri
Farhood Farahnak
George Shenouda
Marie Duclos
Tomás Yokoo Teodoro de Souza
Khalil Sultanem
Parsa Bagherzadeh
Farhad Maleki
Shirin A. Enger
Privacy regulations and limited expert-validation constrain the deployment of large language models (LLMs) for electronic health record stru… (voir plus)cturing. We evaluated locally deployed LLMs to extract 30 prognostic variables from 1,360 head and neck cancer reports (882 patients) using zero-shot prompting. A stratified 50-case subset was reviewed by three radiation oncologists (50 cases, 30 fields, 3 reviewers; 4,500 decisions) to form a majority-vote reference for Llama3.3-70B, which achieved 98.6% F1 with high clinician agreement and processed reports in 53 s/report. Among seven additional models (2.6B-70B) benchmarked against this reference, GPT-OSS-20.9B (F1 89.4%) and MedGemma-27B (F1 88.5%) performed best. Integrating LLM-extracted HPV status, smoking history, and Charlson Comorbidity Score into a multivariate Cox Proportional Hazards model (age, sex, T/N stage) improved disease-free survival (likelihood ratio test p = 0.014; ΔC-index + 0.071) and locoregional failure-free survival (p = 0.026; ΔC-index + 0.108) with 1,000-bootstrap internal validation. This clinician-AI collaborative evaluation shows that on-premises LLMs enable privacy-preserving and efficient tumour board support, longitudinal data curation, and outcome prediction.
2026-06-05
npj Precision Oncology (publié)
doi.org
Source-free Domain Adaptation Requires Penalized Diversity
Laya Rafiee Sevyeri
Ivaxi Sheth
Farhood Farahnak
Alexandre See
Samira Ebrahimi Kahou
Thomas Fevens
Mohammad Havaei
While neural networks are capable of achieving human-like performance in many tasks such as image classification, the impressive performance… (voir plus) of each model is limited to its own dataset. Source-free domain adaptation (SFDA) was introduced to address knowledge transfer between different domains in the absence of source data, thus, increasing data privacy. Diversity in representation space can be vital to a model`s adaptability in varied and difficult domains. In unsupervised SFDA, the diversity is limited to learning a single hypothesis on the source or learning multiple hypotheses with a shared feature extractor. Motivated by the improved predictive performance of ensembles, we propose a novel unsupervised SFDA algorithm that promotes representational diversity through the use of separate feature extractors with Distinct Backbone Architectures (DBA). Although diversity in feature space is increased, the unconstrained mutual information (MI) maximization may potentially introduce amplification of weak hypotheses. Thus we introduce the Weak Hypothesis Penalization (WHP) regularizer as a mitigation strategy. Our work proposes Penalized Diversity (PD) where the synergy of DBA and WHP is applied to unsupervised source-free domain adaptation for covariate shift. In addition, PD is augmented with a weighted MI maximization objective for label distribution shift. Empirical results on natural, synthetic, and medical domains demonstrate the effectiveness of PD under different distributional shifts.
2025-08-25
Machine Learning (publié)
doi.org
arxiv.org
Transparent Anomaly Detection via Concept-based Explanations
Laya Rafiee Sevyeri
Ivaxi Sheth
Farhood Farahnak
S. Enger
2023-10-15
ArXiv (prépublication)
doi.org
arxiv.org
Learning from uncertain concepts via test time interventions
Ivaxi Sheth
Aamer Abdul Rahman
Laya Rafiee Sevyeri
Mohammad Havaei
S Ebrahimi Kahou
With neural networks applied to safety-critical applications, it has become increasingly important to understand the defining features of de… (voir plus)cision-making. Therefore, the need to uncover the black boxes to rational representational space of these neural networks is apparent. Concept bottleneck model (CBM) encourages interpretability by predicting human-understandable concepts. They predict concepts from input images and then labels from concepts. Test time intervention, a salient feature of CBM, allows for human-model interactions. However, these interactions are prone to information leakage and can often be ineffective inappropriate communication with humans. We propose a novel uncertainty based strategy, \emph{SIUL: Single Interventional Uncertainty Learning} to select the interventions. Additionally, we empirically test the robustness of CBM and the effect of SIUL interventions under adversarial attack and distributional shift. Using SIUL, we observe that the interventions suggested lead to meaningful corrections along with mitigation of concept leakage. Extensive experiments on three vision datasets along with a histopathology dataset validate the effectiveness of our interventional learning.
2022-11-19
NeurIPS.cc/2022/Workshop/TSRML (accepté)
openreview.net
openreview.net