Temporal Entailment Pretraining for Clinical Language Models over EHR Data
This addresses the need for better temporal reasoning in clinical AI models, representing a novel method for a known bottleneck in the domain.
The paper tackled the problem of clinical language models ignoring the temporal nature of electronic health records by introducing a temporal entailment pretraining objective, resulting in state-of-the-art performance on tasks like temporal clinical QA and disease progression modeling.
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.