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

Published in The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, 2023

Recommended citation: Timothy Miller, Steven Bethard, Dmitriy Dligach, and Guergana Savova. 2023. End-to-end clinical temporal information extraction with multi-head attention. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 313–319, Toronto, Canada. Association for Computational Linguistics. https://aclanthology.org/2023.bionlp-1.28/

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.