Classification of Veterinary Subjects in Medical Literature and Clinical Summaries...German Association of Medical Informatics (GMDS) 69th Annual Meeting, September 8-11, 2024, Dresden, Germany
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...
| Publicado en: | Studies in Health Technology & Informatics Vol. 317; pp. 210 - 218 |
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| Autores principales: | , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179603616&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179603616 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2024 vid: 317 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 179603616 179603616 179603616 10.3233/SHTI240858 179603616 ppf: 210 ppct: 8 formats: tig: atl: Classification of Veterinary Subjects in Medical Literature and Clinical Summaries...German Association of Medical Informatics (GMDS) 69th Annual Meeting, September 8-11, 2024, Dresden, Germany aug: au: HILTNER, Marcel GULDEN, Christian TODDENROTH, Dennis affil: Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Medical Informatics, Biometrics and Epidemiology, Medical Informatics, Erlangen, Germany sug: subj: Medical Literature Veterinary Medicine Information Retrieval Methods Software Design Evaluation Validity Congresses and Conferences Germany Germany Human PubMed Natural Language Processing Support Vector Machine Artificial Intelligence Data Mining ab: 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 SVMbased one. Discussion: Such classifiers could be applicable in clinical decision support functions that build on automated information retrieval. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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