Summary of Fine-tuning Foundational Models to Code Diagnoses From Veterinary Health Records, by Mayla R. Boguslav et al.
Fine-tuning foundational models to code diagnoses from veterinary health records
by Mayla R. Boguslav, Adam Kiehl, David Kott, G. Joseph Strecker, Tracy Webb, Nadia Saklou, Terri Ward, Michael Kirby
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a method to improve the interoperability of veterinary medical records by leveraging pre-trained large language models (LLMs) and fine-tuning them on electronic health records (EHRs). The authors build upon previous studies that used NLP to automate veterinary diagnosis coding, but this study includes all 7,739 distinct SNOMED-CT diagnosis codes recognized by the Colorado State University Veterinary Teaching Hospital. Ten pre-trained LLMs were fine-tuned on free-text notes from 246,473 manually-coded patient visits, achieving superior performance compared to previous efforts. The results demonstrate that accessible methods for automated coding can improve the quality of veterinary EHRs and support animal and human health research by enabling more integrated and comprehensive databases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps make it easier to share medical records between different places and species. They use special computer models to look at notes from veterinarians and match them with correct medical codes. This makes it easier to understand what’s going on with animals and can help human health research too. The study shows that using big computers and lots of data can make the process work better. |
Keywords
» Artificial intelligence » Fine tuning » Nlp
