A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning

• URL: http://arxiv.org/abs/2607.16681v1
• Date: Sat, 18 Jul 2026 07:27:04 GMT
• Title: A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning
• Abstract summary: Early sepsis prediction from electronic health records is challenged by irregular sampling, high missingness, and class imbalance.<n>We show that self-supervised pretraining with task-aware fine-tuning yields features that are both sharp near onset and robust across prediction horizons.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 dataset. Our best model (JEPA + XGBoost + mean pooling) achieves AUPRC 0.636 at the time of onset (H0), approaching the SupMix benchmark (0.667) while using 83\% fewer biomarkers. The Tier 1 pipeline -- VICReg pretraining followed by semi-supervised fine-tuning and XGBoost -- achieves AUPRC 0.510 at H0, a 3.1$\times$ improvement over the raw-feature baseline (0.165) and a 7.6\% improvement over the end-to-end supervised TCN (0.474). Crucially, the fine-tuned VICReg encoder exhibits the most temporally persistent representations, degrading only 16.8\% from H0 to H10 compared to 47.5\% for supervised TCN and 65.3\% for JEPA, demonstrating that self-supervised pretraining with task-aware fine-tuning yields features that are both sharp near onset and robust across prediction horizons.

Related papers

• Large-Small Model Collaboration for Zero-Shot Surgical Phase Recognition [45.191945465591374]
  Task-specific lightweight models for surgical phase recognition excel at capturing temporal dynamics but generalize poorly under domain shift.<n>We propose textbfLarge-textbfSmall textbfTemporal adaptation (textbfLaST), a novel large-small collaborative framework that enables zero-shot adaptation to unseen clinical domains.
  arXiv  Detail & Related papers  (2026-08-24T07:06:52Z)
• From Seasonality to Semantics: Benchmarking a Hybrid Probabilistic Forecasting System for Roadblocks in Bolivia [0.0]
  Roadblocks in Bolivia are a social conflict phenomenon with devastating economic impacts.<n>Despite their recurrence and impact, there is a lack of local predictive systems to anticipate these events.<n>This paper presents a hybrid probabilistic forecasting system that integrates time series decomposition and natural language processing.
  arXiv  Detail & Related papers  (2026-07-23T20:05:48Z)
• Scaling Closed-Loop Feature Channel Configuration with LLMs [48.83701310501069]
  The analysis covers 2000 generated candidates from 8 complete cycles, yielding 462 verified CIFAR-100 evaluations after task and metadata filtering.<n>The best model reaches 0.3676 with 11.8M parameters, compared with an early high-performing model at 0.3144 with 166.5M parameters.<n>Non-power-of-two channel widths occur in 41.8% of verified candidates, and the strongest models share structured channelallocation patterns.
  arXiv  Detail & Related papers  (2026-07-07T22:31:53Z)
• LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning [59.34593956287438]
  We study how an SSL recipe behaves when its method-specific configuration is reused unchanged after the pretraining signal family changes.<n>We introduce Latent Euclidean Next-Embedding Prediction Architecture (LeNEPA)<n>LeNEPA replaces the stop-gradient/EMA stabilization used by vanilla NEPA with SIGReg-based isotropy regularization.
  arXiv  Detail & Related papers  (2026-07-01T13:56:21Z)
• Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories [7.992399976910451]
  Clin-JEPA is a multi-phase co-training framework for predictive (JEPA) pretraining on EHR patient trajectories.<n>On MIMIC-IV ICU data, three independent evaluations support the framework.
  arXiv  Detail & Related papers  (2026-05-11T16:54:23Z)
• Tyche: One Step Flow for Efficient Probabilistic Weather Forecasting [48.263481135566316]
  We present Tyche, a one-step conditional flow model for efficient probabilistic weather forecasting.<n>Experiments on ERA5 at 1.5$circ$ and 6-hour resolution show that our Tyche, using merely a single NFE, matches or exceeds the forecast skill and calibration of state-of-the-art multi-step generative baselines.
  arXiv  Detail & Related papers  (2026-05-07T20:25:28Z)
• SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting [0.5076419064097734]
  Generative models for time-series imputation achieve strong reconstruction accuracy, yet provide no finite-sample reliability guarantees.<n>We introduce SPLICE, a modular framework coupling latent generative imputation with distribution-free, online-adaptive prediction intervals.<n>A pooled JEPA encoder trained on nine feeds transfers to four unseen domains, matching or exceeding per-dataset oracles with only a quick bridge fine-tuning.
  arXiv  Detail & Related papers  (2026-04-30T18:27:08Z)
• Differentiable latent structure discovery for interpretable forecasting in clinical time series [38.473526928012724]
  We introduce StructGP, a continuous-time multi-task Gaussian process that couples differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty.<n>We propose LP-StructGP, which augments StructGP with latent pathways-shared, temporally shifted trajectories inferred via subject-specific coupling filters and a softmax gating mechanism-to capture cross-patient progression patterns.
  arXiv  Detail & Related papers  (2026-04-30T14:59:50Z)
• Meteorology-Driven GPT4AP: A Multi-Task Forecasting LLM for Atmospheric Air Pollution in Data-Scarce Settings [4.364999214109123]
  Meteorology-Driven GPT for Air Pollution (GPT4AP) based on a pre-trained GPT-2 backbone and Gaussian rank-stabilized low-rank adaptation (rsLoRA)<n>Model is evaluated on six real-world air quality monitoring datasets under few-shot, zero-shot, and long-term forecasting settings.
  arXiv  Detail & Related papers  (2026-03-31T16:37:06Z)
• SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction [46.645872209565574]
  We present a specialized large-scale foundation model for virtual cell perturbation prediction.<n>First, we build a BioNeMo-based training and inference framework that substantially improves data throughput.<n>Second, we formulate perturbation prediction as conditional transport and implement it with a set-aware flow architecture.<n>Third, we evaluate the model on Tahoe-100M using a rigorous cell-level protocol centered on biologically meaningful metrics.
  arXiv  Detail & Related papers  (2026-03-18T05:51:08Z)
• Do Generative Metrics Predict YOLO Performance? An Evaluation Across Models, Augmentation Ratios, and Dataset Complexity [43.338311770275745]
  We present a controlled evaluation of synthetic augmentation for YOLOv11 across three single-class detection regimes.<n>We benchmark six GAN-, diffusion-, and hybrid-based generators over augmentation ratios from 10% to 150% of the real training split.<n>For each dataset-generator-augmentation configuration, we compute pre-training dataset metrics under a matched-size bootstrap protocol.
  arXiv  Detail & Related papers  (2026-02-20T03:02:36Z)
• Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning [70.56067503630486]
  We argue that sixth-generation (6G) intelligence is not fluent token prediction but calibrated the capacity to imagine and choose.<n>We show that WM-MS3M cuts mean absolute error (MAE) by 1.69% versus MS3M with 32% fewer parameters and similar latency, and achieves 35-80% lower root mean squared error (RMSE) than attention/hybrid baselines with 2.3-4.1x faster inference.
  arXiv  Detail & Related papers  (2025-11-04T17:22:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.