Defining and learning refined temporal relations in the clinical narrative
Published in Proceedings of the 11th International Workshop on Health Text Mining and …, 2020, 2020
Recommended citation: Kristin Wright-Bettner, Chen Lin, Timothy A Miller, Steven Bethard, Dmitriy Dligach, Martha Palmer, James H Martin, and Guergana K Savova. 2020. Defining and learning refined temporal relations in the clinical narrative. In Proceedings of the 11th International Workshop on Health Text Mining and …, 2020. https://aclanthology.org/2020.louhi-1.12.pdf
Abstract:
We present refinements over existing temporal relation annotations in the Electronic Medical Record clinical narrative. We refined the THYME corpus annotations to more faithfully represent nuanced temporality and nuanced temporal-coreferential relations. The main contributions are in re-defining CONTAINS and OVERLAP relations into CONTAINS, CONTAINS-SUBEVENT, OVERLAP and NOTED-ON. We demonstrate that these refinements lead to substantial gains in learnability for state-of-the-art transformer models as compared to previously reported results on the original THYME corpus. We thus establish a baseline for the automatic extraction of these refined temporal relations. Although our study is done on clinical narrative, we believe it addresses far-reaching challenges that are corpus-and domain-agnostic.