3 citations · 6 across the 15 of their papers we have counts for
10 papers · 1 filter
Causal Machine Learning Is Not a Panacea: A Roadmap for Observational Causal Inference in Health
Donna Tjandra, Trenton Chang, Sonali Parbhoo +8
Objective: The growing availability of large-scale observational clinical datasets and challenges in conducting randomized controlled trials have spurred enthusiasm in using causal…
Uncertainty Makes It Stable: Curiosity-Driven Quantized Mixture-of-Experts
Sebastián Andrés Cajas Ordóñez, Luis Fernando Torres Torres, Mackenzie J. Meni +6
Deploying deep neural networks on resource-constrained devices faces two critical challenges: maintaining accuracy under aggressive quantization while ensuring predictable inferenc…
Learning Representations from Incomplete EHR Data with Dual-Masked Autoencoding
Xiao Xiang, David Restrepo, Hyewon Jeong +2
Electronic health records (EHR) arrive masked. Clinicians order measurements selectively, and any patient table thus contains only a subset of the values that characterize the unde…
Coefficient of Variation Masking: A Volatility-Aware Strategy for EHR Foundation Models
Rajna Fani, Rafi Al Attrach, David Restrepo +3
Masked autoencoders (MAEs) are increasingly applied to electronic health records (EHR) for learning general-purpose representations that support diverse clinical tasks. However, ex…
Uncertainty-Aware Generative Oversampling Using an Entropy-Guided Conditional Variational Autoencoder
Amirhossein Zare, Amirhessam Zare, Parmida Sadat Pezeshki +5
Class imbalance remains a major challenge in machine learning, especially for high-dimensional biomedical data where nonlinear manifold structures dominate. Traditional oversamplin…
Representation Learning of Lab Values via Masked AutoEncoders
David Restrepo, Chenwei Wu, Yueran Jia +5
Accurate imputation of missing laboratory values in electronic health records (EHRs) is critical to enable robust clinical predictions and reduce biases in AI systems in healthcare…