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Fine-tuning foundational models to code diagnoses from veterinary health records.

Mayla R Boguslav1,2, Adam Kiehl3, David Kott1

  • 1Data Science Research Institute, Colorado State University, Fort Collins, Colorado, United States of America.

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Summary

Automated coding of veterinary diagnoses using Natural Language Processing (NLP) significantly improved electronic health record (EHR) data quality. This advancement supports integrated animal and human health research by enabling comprehensive, cross-institutional databases.

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Area of Science:

  • Veterinary Informatics
  • Computational Linguistics
  • One Health Research

Background:

  • Veterinary medical records are valuable for research but face interoperability issues due to inconsistent formats and data siloing.
  • Standardized medical terminologies and clinical coding enhance record quality and facilitate data sharing across institutions and species.
  • Previous Natural Language Processing (NLP) studies automated subsets of veterinary diagnosis codes using specific models like LSTM and transformers.

Purpose of the Study:

  • To expand automated veterinary diagnosis coding to include all 7,739 Systemized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) codes.
  • To evaluate the performance of 13 diverse pre-trained language models (LMs) for automated coding using veterinary electronic health records (EHRs).
  • To assess the impact of data volume and LM size/type on coding accuracy.

Main Methods:

  • Fine-tuned 13 distinct pre-trained language models (LMs) on free-text clinical notes from 246,473 veterinary patient visits.
  • Utilized a comprehensive set of 7,739 SNOMED-CT diagnosis codes from Colorado State University Veterinary Teaching Hospital (CSU VTH) EHRs.
  • Compared the performance of various LMs, including clinical and non-clinical models, with different amounts of labeled data.

Main Results:

  • The fine-tuned LMs demonstrated superior performance in automated veterinary diagnosis coding compared to previous efforts.
  • Optimal accuracy was achieved using large clinical LMs fine-tuned on extensive labeled data.
  • Comparable results were attainable with more limited resources and non-clinical LMs, indicating accessible methods for automated coding.

Conclusions:

  • Automated coding using advanced NLP techniques significantly enhances veterinary EHR quality and interoperability.
  • This approach facilitates the creation of integrated, cross-species health databases, advancing veterinary and One Health research.
  • Accessible methods for automated coding improve data utility and support collaborative research efforts across institutions.