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Hakima Laribi

hakima.laribi@mila.quebec
Doctorat - Université de Sherbrooke
Superviseur⋅e principal⋅e
Dan Poenaru
Co-supervisor
Martin Vallières
Sujets de recherche
Apprentissage automatique médical
Apprentissage multimodal
Apprentissage profond
Apprentissage sur graphes
Réseaux de neurones en graphes
Site web
GitHub

Publications

Diagnosing Low-Confidence Patient Profiles via Localized Harmful Covariate Shift Detection
Olivier Lefebvre
Hakima Laribi
Dan Poenaru
Martin Vallières
Before deploying clinical prediction models, their reliability must be assessed to ensure safe use. Errors may arise from distribution shift… (voir plus)s between training and deployment or from heterogeneous performance where the model systematically underperforms for certain patient subgroups. Yet most post-hoc diagnostic methods evaluate reliability only at the population level, checking whether the deployment data resemble the training data and whether the model performs well on average, without accounting for heterogeneity that may cause some patients to be disproportionately affected. We propose a unified diagnostic pipeline that combines both perspectives. MED3pa first discovers interpretable low-confidence patient profiles, and Detectron is then applied within each profile to test whether its degradation is associated with a detectable harmful covariate shift. Across three clinical prediction tasks and three injected subgroup shifts in MIMIC-IV, we find that shift detectability itself is informative. An injected age shift, undetectable at the population level, was nonetheless localized to specific degraded profiles, whereas sex- and race-related shifts were detected broadly and therefore offered little guidance for identifying the most affected patients.
2026-10-12
arXiv (prépublication)
doi.org
arxiv.org
Development of Error Passing Network for Optimizing the Prediction of VO$_2$ peak in Childhood Acute Leukemia Survivors
Nicolas Raymond
Hakima Laribi
Maxime Caru
Mehdi Mitiche
Valerie Marcil
Maja Krajinovic
Daniel Curnier
Daniel Sinnett
Martin Vallières
Approximately two-thirds of survivors of childhood acute lymphoblastic leukemia (ALL) cancer develop late adverse effects post-treatment. Pr… (voir plus)ior studies explored prediction models for personalized follow-up, but none integrated the usage of neural networks to date. In this work, we propose the Error Passing Network (EPN), a graph-based method that leverages relationships between samples to propagate residuals and adjust predictions of any machine learning model. We tested our approach to estimate patients’ \vo peak, a reliable indicator of their cardiac health. We used the EPN in conjunction with several baseline models and observed up to 12.16% improvement in the mean average percentage error compared to the last established equation predicting \vo peak in childhood ALL survivors. Along with this performance improvement, our final model is more efficient considering that it relies only on clinical variables that can be self-reported by patients, therefore removing the previous need of executing a resource-consuming physical test.
2024-07-23
Proceedings of the fifth Conference on Health, Inference, and Learning (publié)
proceedings.mlr.press
proceedings.mlr.press