A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning
Early sepsis prediction from electronic health records is challenged by irregular sampling, high missingness, and class imbalance. We systematically compare four modeling paradigms -- self-supervised Joint Embedding Predictive Architecture (JEPA) via masked latent prediction, self-supervised VICReg (variance-invariance-covariance regularization) with two-view augmentation, semi-supervised fine-tuning of a VICReg-pretrained encoder, and supervised Temporal Convolutional Network (TCN) -- alongside raw-feature baselines. All models share a common preprocessing pipeline of hourly binning with forward-fill imputation applied to 7 biomarkers selected via sparsity analysis from the MIMIC-III dat...
| Authors | Umair bin Mansoor, Munaf Rashid, Roomi Naqvi |
|---|---|
| Primary category | cs.LG |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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