Classification of Veterinary Subjects in Medical Literature and Clinical Summaries.

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.