Literature-Augmented Clinical Outcome Prediction
This work addresses the problem of improving predictive accuracy in clinical outcomes for healthcare applications, representing an incremental advancement by enhancing existing models with literature retrieval.
The paper tackles clinical outcome prediction by retrieving patient-specific medical literature and integrating it with clinical notes, achieving up to a 5-point increase in F1 and over 25% improvement in precision@Top-K compared to strong language model baselines.
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%.