Literature-Augmented Clinical Outcome Prediction

Anonymous

Paper Link: https://openreview.net/forum?id=wcTzgV4y9gU

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

Abstract: 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%.

Presentation Mode: This paper will be presented in person in Seattle

Copyright Consent Signature (type Name Or NA If Not Transferrable): Aakanksha Naik

Copyright Consent Name And Address: Allen Institute for Artificial Intelligence, 2157 N Northlake Way, Suite 110, Seattle, WA - 98103