Archer Yang

Kent Lu

Maîtrise recherche - McGill

Marshall Meng

Maîtrise recherche - McGill

Co-superviseur⋅e :

Camille Morence

Maîtrise recherche - McGill

Francois Rocheleau

Maîtrise recherche - McGill

Weihua Shi

Doctorat - McGill

Jack Song

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Github

Harri Vanhems

Baccalauréat - McGill

Kunpeng Xu

Postdoctorat - McGill

Superviseur⋅e principal⋅e :

Publications

Multivariate Conformal Selection

Tian Bai

Yue Zhao

Xiang Yu

Selecting high-quality candidates from large datasets is critical in applications such as drug discovery, precision medicine, and alignment … (voir plus)of large language models (LLMs). While Conformal Selection (CS) provides rigorous uncertainty quantification, it is limited to univariate responses and scalar criteria. To address this, we propose Multivariate Conformal Selection (mCS), a generalization of CS designed for multivariate response settings. Our method introduces regional monotonicity and employs multivariate nonconformity scores to construct conformal

2025-10-05

Proceedings of the 42nd International Conference on Machine Learning (publié)

proceedings.mlr.press

Sparse Polygenic Risk Score Inference with the Spike-and-Slab LASSO

Junyi Song

Shadi Zabad

Simon Gravel

Yuemei Li

2024-12-31

Bioinform. (publié)

Abstract 4142894: Multimorbidity Trajectories Across the Lifespan in Patients with Congenital Heart Disease

Chao Li

Aihua Liu

Solomon Bendayan

Liming Guo

Judith Therrien

Robyn Tamblyn

Jay Brophy

Yuemei Li

Ariane Marelli

Background: Befitted from advances in medical care, patients with congenital heart disease (CHD) now survive to adulthood but face elevated… (voir plus) risks of both cardiac and non-cardiac complications. Understanding the trajectories of comorbidity development over a patient's lifespan is cornerstone to optimize care expected to improve long-term health outcomes. Research Aim: This study aims to investigate the temporal sequences and evolution of comorbidities in CHD patients across their lifespan. We hypothesize that multimorbidity trajectories in CHD patients are linked to CHD lesion severity and age at onset of specific comorbidities. Methods: Using the Quebec CHD database which comprised data in outpatient visits, hospitalization records and vital status from 1983 to 2017, we designed a longitudinal cohort study evaluating the development of 39 comorbidities coded using ICD-9/10. Temporal sequences were mapped using median age of onset. Associations between disease pairs were quantified by hazard ratios from Cox proportional hazard models adjusting for age, sex, genetic syndrome, competing risks of death, and taking into account the time-varying nature of the predictor diseases. Results: The cohort included 9,764 individuals with severe and 127,729 with non-severe CHD lesions. In severe CHD patients, most comorbidities developed between ages 25 and 40. Comorbidity progression began with childhood cardiovascular diseases, followed by systemic diseases such as diabetes, liver and kidney diseases, and advanced to heart failure and dementia in middle adulthood. In addition, mental disorders emerged in early adulthood and were associated with subsequent development of kidney diseases and dementia. Different trajectories were observed in non-severe CHD patients with 2-3 decades later disease onsets and non-differential onsets between cardiovascular and systemic complications (Figure). Conclusions: Distinct multimorbidity trajectories were observed in CHD patients by CHD lesion severity. In patients with severe CHD lesions, early systemic diseases significantly influenced subsequent complications. These findings highlight the need for well-timed surveillance guidelines and interventions to improve health outcomes.

2024-11-11

Circulation (publié)

Privacy-preserving analysis of time-to-event data under nested case-control sampling

Lamin Juwara

Ana M Velly

Paramita Saha-Chaudhuri

2023-12-12

Statistical Methods in Medical Research (publié)

A Tweedie Compound Poisson Model in Reproducing Kernel Hilbert Space

Yi Lian

Boxiang Wang

Peng Shi

Robert William Platt

Abstract Tweedie models can be used to analyze nonnegative continuous data with a probability mass at zero. There have been wide application… (voir plus)s in natural science, healthcare research, actuarial science, and other fields. The performance of existing Tweedie models can be limited on today’s complex data problems with challenging characteristics such as nonlinear effects, high-order interactions, high-dimensionality and sparsity. In this article, we propose a kernel Tweedie model, Ktweedie, and its sparse variant, SKtweedie, that can simultaneously address the above challenges. Specifically, nonlinear effects and high-order interactions can be flexibly represented through a wide range of kernel functions, which is fully learned from the data; In addition, while the Ktweedie can handle high-dimensional data, the SKtweedie with integrated variable selection can further improve the interpretability. We perform extensive simulation studies to justify the prediction and variable selection accuracy of our method, and demonstrate the applications in ratemaking and loss-reserving in general insurance. Overall, the Ktweedie and SKtweedie outperform existing Tweedie models when there exist nonlinear effects and high-order interactions, particularly when the dimensionality is high relative to the sample size. The model is implemented in an efficient and user-friendly R package ktweedie (https://cran.r-project.org/package=ktweedie).

2022-12-12

Technometrics (publié)