Hiltner M, Gulden C, Toddenroth D (2024)
Publication Type: Conference contribution
Publication year: 2024
Publisher: IOS Press BV
Book Volume: 317
Pages Range: 210-217
Conference Proceedings Title: Studies in Health Technology and Informatics
Event location: Dresden, DEU
ISBN: 9781643685366
DOI: 10.3233/SHTI240858
Introduction: Human and veterinary medicine are practiced separately, but literature databases such as Pubmed include articles from both fields. This impedes supporting clinical decisions with automated information retrieval, because treatment considerations would not ignore the discipline of mixed sources. Here we investigate data-driven methods from computational linguistics for automatically distinguishing between human and veterinary medical texts. Methods: For our experiments, we selected language models after a literature review of benchmark datasets and reported performances. We generated a dataset of around 48,000 samples for binary text classification, specifically designed to differentiate between human medical and veterinary subjects. Using this dataset, we trained and fine-tuned classifiers based on selected transformer-based models as well as support vector machines (SVM). Results: All trained classifiers achieved more than 99% accuracy, even though the transformer-based classifiers moderately outperformed the SVM-based one. Discussion: Such classifiers could be applicable in clinical decision support functions that build on automated information retrieval.
APA:
Hiltner, M., Gulden, C., & Toddenroth, D. (2024). Classification of Veterinary Subjects in Medical Literature and Clinical Summaries. In Rainer Rohrig, Niels Grabe, Ursula Hertha Hubner, Klaus Jung, Ulrich Sax, Carsten Oliver Schmidt, Martin Sedlmayr, Antonia Zapf (Eds.), Studies in Health Technology and Informatics (pp. 210-217). Dresden, DEU: IOS Press BV.
MLA:
Hiltner, Marcel, Christian Gulden, and Dennis Toddenroth. "Classification of Veterinary Subjects in Medical Literature and Clinical Summaries." Proceedings of the 69th Annual Meeting of the German Association of Medical Informatics, Biometry and Epidemiology, GMDS 2024, Dresden, DEU Ed. Rainer Rohrig, Niels Grabe, Ursula Hertha Hubner, Klaus Jung, Ulrich Sax, Carsten Oliver Schmidt, Martin Sedlmayr, Antonia Zapf, IOS Press BV, 2024. 210-217.
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