ArticleFrontiers in veterinary science2024
Text mining for disease surveillance in veterinary clinical data: part one, the language of veterinary clinical records and searching for words.
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 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- 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
- Epidemiology of Four Major Canine Tumours in the UK: Insights From a National Pathology Registry With Comparative Oncology Perspectives.Veterinary and comparative oncology · 2026Article
- A socio-ecological approach to the determinants of animal health management: A scoping review.PloS one · 2026Article
- First opinion practice electronic health records are a useful source of descriptions of medication errors.Frontiers in veterinary science · 2025Article
- Premature mortality analysis of 52,000 deceased cats and dogs exposes socioeconomic disparities.Scientific reports · 2024Article
- Development and validation of a machine learning model for clinical wellness visit classification in cats and dogs.Frontiers in veterinary science · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of natural language processing techniques for deriving useful information from unstructured clinical narratives is a fast-paced and rapidly evolving area of machine learning research. Large volumes of veterinary clinical narratives now exist curated by projects such as the Small Animal Veterinary Surveillance Network (SAVSNET) and VetCompass, and the application of such techniques to these datasets is already (and will continue to) improve our understanding of disease and disease patterns within veterinary medicine. In part one of this two part article series, we discuss the importance of understanding the lexical structure of clinical records and discuss the use of basic tools for filtering records based on key words and more complex rule based pattern matching approaches. We discuss the strengths and weaknesses of these approaches highlighting the on-going potential value in using these "traditional" approaches but ultimately recognizing that these approaches constrain how effectively information retrieval can be automated. This sets the scene for the introduction of machine-learning methodologies and the plethora of opportunities for automation of information extraction these present which is discussed in part two of the series.
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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.