Literature-Augmented Clinical Outcome Prediction

Aakanksha Naik, Sravanthi Parasa, Sergey Feldman, Lucy Lu Wang, Tom Hope

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

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.1 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%.

Original languageAmerican English
Title of host publicationFindings of the Association for Computational Linguistics
Subtitle of host publicationNAACL 2022 - Findings
PublisherAssociation for Computational Linguistics (ACL)
Pages438-453
Number of pages16
ISBN (Electronic)9781955917766
StatePublished - 2022
Externally publishedYes
Event2022 Findings of the Association for Computational Linguistics: NAACL 2022 - Seattle, United States
Duration: 10 Jul 202215 Jul 2022

Publication series

NameFindings of the Association for Computational Linguistics: NAACL 2022 - Findings

Conference

Conference2022 Findings of the Association for Computational Linguistics: NAACL 2022
Country/TerritoryUnited States
CitySeattle
Period10/07/2215/07/22

Bibliographical note

Publisher Copyright:
© Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

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