Temporal Entailment Pretraining for Clinical Language Models over EHR Data
Journal:
arXiv
Published Date:
Apr 25, 2025
Abstract
Clinical language models have achieved strong performance on downstream tasks
by pretraining on domain specific corpora such as discharge summaries and
medical notes. However, most approaches treat the electronic health record as a
static document, neglecting the temporally-evolving and causally entwined
nature of patient trajectories. In this paper, we introduce a novel temporal
entailment pretraining objective for language models in the clinical domain.
Our method formulates EHR segments as temporally ordered sentence pairs and
trains the model to determine whether a later state is entailed by,
contradictory to, or neutral with respect to an earlier state. Through this
temporally structured pretraining task, models learn to perform latent clinical
reasoning over time, improving their ability to generalize across forecasting
and diagnosis tasks. We pretrain on a large corpus derived from MIMIC IV and
demonstrate state of the art results on temporal clinical QA, early warning
prediction, and disease progression modeling.
