ArticleFrontiers in veterinary science2024
Text mining for disease surveillance in veterinary clinical data: part two, training computers to identify features in clinical text.
Article in Frontiers in veterinary science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Important bioactives carnitine and betaine in milk and dairy products: a systematic review based on text mining and topic analysis.Food science of animal resources · 2026Article
- Article
- Reusing health records from farm animal practices at scale: A potential complementary method of surveillance.The Veterinary record · 2026Article
- Development and implementation of a data parsing protocol for companion animal cancer data.Veterinary pathology · 2026Article
- Intestinal Dysbiosis Relating to Gut-Brain Axis and Behavior in Dogs: A Systematic Review with Text Mining Approach.Animals : an open access journal from MDPI · 2026Review
- First opinion practice electronic health records are a useful source of descriptions of medication errors.Frontiers in veterinary science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.