PORTER is a language-grounded EHR foundation model that uses text descriptions for events and a numeric pathway, matching fixed-vocabulary performance on 74 tasks while recovering 97.1% AUROC on unseen vocabularies and outperforming on MIMIC.
12.Li, Y .et al.Behrt: transformer for electronic health records.Scientific reports10, 7155 (2020)
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RAVEN pretrains on over one million EHR sequences via recurrence-aware next-visit event prediction, enabling zero-shot disease incidence forecasting that rivals fine-tuned models and generalizes across cohorts.
Hybrid token-based binning for numeric values in EHR transformers is more robust than explicit interaction modeling, with optimal bin count following an empirically derived power-law in dataset size.
citing papers explorer
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PORTER: Language-Grounded Event Representations for Portable Structured EHR Foundation Models
PORTER is a language-grounded EHR foundation model that uses text descriptions for events and a numeric pathway, matching fixed-vocabulary performance on 74 tasks while recovering 97.1% AUROC on unseen vocabularies and outperforming on MIMIC.
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Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction
RAVEN pretrains on over one million EHR sequences via recurrence-aware next-visit event prediction, enabling zero-shot disease incidence forecasting that rivals fine-tuned models and generalizes across cohorts.
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How Should Transformers Encode Numeric Values in Electronic Health Records?
Hybrid token-based binning for numeric values in EHR transformers is more robust than explicit interaction modeling, with optimal bin count following an empirically derived power-law in dataset size.