Text mining for disease surveillance in veterinary clinical data: part two, training computers to identify features in clinical text.


Davies, Heather, Nenadic, Goran, Alfattni, Ghada, Arguello Casteleiro, Mercedes, Al Moubayed, Noura, Farrell, Sean, Radford, Alan D and Noble, P-JM ORCID: 0000-0002-2275-2014 (2024) Text mining for disease surveillance in veterinary clinical data: part two, training computers to identify features in clinical text. Frontiers in veterinary science, 11. p. 1352726. ISSN 2297-1769, 2297-1769

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Abstract

In part two of this mini-series, we evaluate the range of machine-learning tools now available for application to veterinary clinical text-mining. These tools will be vital to automate extraction of information from large datasets of veterinary clinical narratives curated by projects such as the Small Animal Veterinary Surveillance Network (SAVSNET) and VetCompass, where volumes of millions of records preclude reading records and the complexities of clinical notes limit usefulness of more "traditional" text-mining approaches. We discuss the application of various machine learning techniques ranging from simple models for identifying words and phrases with similar meanings to expand lexicons for keyword searching, to the use of more complex language models. Specifically, we describe the use of language models for record annotation, unsupervised approaches for identifying topics within large datasets, and discuss more recent developments in the area of generative models (such as ChatGPT). As these models become increasingly complex it is pertinent that researchers and clinicians work together to ensure that the outputs of these models are explainable in order to instill confidence in any conclusions drawn from them.

Item Type: Article
Uncontrolled Keywords: big data, machine learning, neural language modeling, clinical records, companion animals
Depositing User: Symplectic Admin
Date Deposited: 13 Sep 2024 13:31
Last Modified: 23 May 2026 09:16
DOI: 10.3389/fvets.2024.1352726
Open Access URL: https://doi.org/10.3389/fvets.2024.1352726
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URI: https://livrepository.liverpool.ac.uk/id/eprint/3184483
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