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Foundation models for electronic health records: representation dynamics and transferability
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Foundation models for electronic health records: representation dynamics and transferability
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Foundation models (FMs) trained on electronic health records (EHRs) have shown strong performance on a range of clinical prediction tasks. However, adapting these models to local health systems remains challenging due to limited data availability and resource constraints. In this study, we investigated what these models learn and evaluated the transferability of an FM trained on MIMIC-IV to an institutional EHR dataset at the University of Chicago Medical Center. We assessed their ability to identify outlier patients and examined representation-space patient trajectories in relation to future clinical outcomes. We also evaluated the performance of supervised fine-tuned classifiers on both source and target datasets. Our findings offer insights into the adaptability of FMs across different healthcare systems, highlight considerations for their effective implementation, and provide an empirical analysis of the underlying factors that contribute to their predictive performance.
Forward citations
Cited by 11 Pith papers
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Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories
Clin-JEPA supplies a multi-phase co-training method for JEPA pretraining on EHR trajectories that achieves converging latent rollouts and improved multi-task AUROC on MIMIC-IV data.
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Expanders Meet Reed-Muller: Easy Instances of Noisy k-XOR
Explicit near-optimal expanders exist for which noisy k-XOR is polynomial-time solvable, falsifying conjectures that expansion implies hardness.
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AURORA: Contextual Orthogonalization for Geometric Representation Learning in Healthcare Foundation Models
AURORA is a representation learning framework that uses contextual orthogonalization and relational alignment to create disentangled, geometrically interpretable latent spaces in healthcare foundation models.
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Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories
A five-phase co-training framework enables stable JEPA pretraining on EHR trajectories, producing converging latent rollouts and higher multi-task AUROC than baselines on MIMIC-IV ICU data.
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Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories
Clin-JEPA is a multi-phase co-training framework for JEPA pretraining on EHR data that achieves convergent latent rollouts and improved multi-task AUROC on MIMIC-IV ICU records.
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Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories
A five-phase JEPA co-training curriculum on EHR trajectories is claimed to yield stable latent rollouts and a single multi-task clinical backbone on MIMIC-IV.
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Event Fields: Learning Latent Event Structure for Waveform Foundation Models
Event-centric waveform foundation models are learned via self-supervised consistency on latent event structures and interactions, yielding improved performance and label efficiency over sequence-based baselines on phy...
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Uncertainty-Aware Foundation Models for Clinical Data
The work introduces uncertainty-aware foundation models for clinical data by learning set-valued patient representations that enforce consistency across partial observations and integrate multimodal self-supervised ob...
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MorphologyFM: A Foundation Model for Morphology-Aware Representation Learning from ECG and Pulse Oximetry Waveforms
Morphology-aware masking plus cross-modal ECG–SpO2 pretraining on MIMIC yields stronger transfer than MAE, contrastive, Barlow Twins, and JEPA on several clinical prediction tasks.
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WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Records
WISTERIA learns robust clinical representations from noisy EHR labels by enforcing consistency across multiple weak supervision views plus ontology regularization.
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Representation Before Training: A Fixed-Budget Benchmark for Generative Medical Event Models
Fused code-value tokenization improves mortality AUROC from 0.891 to 0.915 and other clinical outcome predictions, while certain temporal encodings like event order match or exceed time tokens with shorter sequences.
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