End-to-end clinical temporal information extraction with multi-head attention

Timothy Miller, Steven Bethard, Dmitriy Dligach, Guergana Savova

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Understanding temporal relationships in text from electronic health records can be valuable for many important downstream clinical applications. Since Clinical TempEval 2017, there has been little work on end-to-end systems for temporal relation extraction, with most work focused on the setting where gold standard events and time expressions are given. In this work, we make use of a novel multi-headed attention mechanism on top of a pre-trained transformer encoder to allow the learning process to attend to multiple aspects of the contextualized embeddings. Our system achieves state of the art results on the THYME corpus by a wide margin, in both the in-domain and cross-domain settings.

Original languageEnglish (US)
Title of host publicationBioNLP 2023 - BioNLP and BioNLP-ST, Proceedings of the Workshop
EditorsDina Demner-fushman, Sophia Ananiadou, Kevin Cohen
PublisherAssociation for Computational Linguistics (ACL)
Pages313-319
Number of pages7
ISBN (Electronic)9781959429852
StatePublished - 2023
Event22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, BioNLP 2023 - Toronto, Canada
Duration: Jul 13 2023 → …

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, BioNLP 2023
Country/TerritoryCanada
CityToronto
Period7/13/23 → …

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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