  We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach
for clinical outcome prediction that retrieves patient-specific medical
literature and incorporates it into predictive models. Based on each individual
patient's clinical notes, we train language models (LMs) to find relevant
papers and fuse them with information from notes to predict outcomes such as
in-hospital mortality. We develop methods to retrieve literature based on
noisy, information-dense patient notes, and to augment existing outcome
prediction models with retrieved papers in a manner that maximizes predictive
accuracy. Our approach boosts predictive performance on three important
clinical tasks in comparison to strong recent LM baselines, increasing F1 by up
to 5 points and precision@Top-K by a large margin of over 25%.
