Text mining for disease surveillance in veterinary clinical data: part two, training computers to identify features in clinical text

Davies, H., Nenadic, G., Alfattni, G., Casteleiro, M. A., Al Moubayed, N., Farrell, S., Radford, A. D., & Noble, P.-J. M. (2024). Text mining for disease surveillance in veterinary clinical data: part two, training computers to identify features in clinical text. Frontiers in Veterinary Science, 11. https://doi.org/10.3389/fvets.2024.1352726

This article evaluates machine-learning tools for text mining in veterinary clinical data. The focus is on automating information extraction from large datasets, such as those from SAVSNET and VetCompass, due to the impracticality of manually reviewing millions of clinical records. Various machine-learning techniques are discussed, from simple models for expanding keyword lexicons to complex language models for record annotation and topic identification. The authors emphasize the importance of explainability in these models to ensure their reliability in clinical applications.