Word sense disambiguation of acronyms in clinical narratives

Chopard, Daphné, Corcoran, Padraig ORCID: https://orcid.org/0000-0001-9731-3385 and Spasić, Irena ORCID: https://orcid.org/0000-0002-8132-3885 2024. Word sense disambiguation of acronyms in clinical narratives. Frontiers in Digital Health 6 , 1282043. 10.3389/fdgth.2024.1282043

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Abstract

Clinical narratives commonly use acronyms without explicitly defining their long forms. This makes it difficult to automatically interpret their sense as acronyms tend to be highly ambiguous. Supervised learning approaches to their disambiguation in the clinical domain are hindered by issues associated with patient privacy and manual annotation, which limit the size and diversity of training data. In this study, we demonstrate how scientific abstracts can be utilised to overcome these issues by creating a large automatically annotated dataset of artificially simulated global acronyms. A neural network trained on such a dataset achieved the F1-score of 95% on disambiguation of acronym mentions in scientific abstracts. This network was integrated with multi-word term recognition to extract a sense inventory of acronyms from a corpus of clinical narratives on the fly. Acronym sense extraction achieved the F1-score of 74% on a corpus of radiology reports. In clinical practice, the suggested approach can be used to facilitate development of institution-specific inventories.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Publisher:	Frontiers Media
ISSN:	2673-253X
Date of First Compliant Deposit:	27 February 2024
Date of Acceptance:	16 February 2024
Last Modified:	13 Mar 2024 10:20
URI:	https://orca.cardiff.ac.uk/id/eprint/166338

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