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DeepTag: inferring diagnoses from veterinary clinical notes.

Allen Nie1, Ashley Zehnder1, Rodney L Page2

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DeepTag, a deep learning algorithm, automatically infers diagnostic codes from veterinary free-text notes, reducing the burden on clinicians and aiding translational research. This AI tool improves patient care by enabling automated disease annotation from clinical records.

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Health servicesPublic health

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Area of Science:

  • Veterinary Medicine
  • Artificial Intelligence
  • Biomedical Informatics

Background:

  • Veterinary clinical records are valuable but underutilized due to the lack of standardized diagnostic coding.
  • Free-text notes dominate veterinary records, posing challenges for data analysis and research.
  • Accurate coding is crucial for improving patient care and enabling cross-species translational research.

Purpose of the Study:

  • To develop an automated system for inferring diagnostic codes from veterinary free-text notes.
  • To reduce the manual coding burden on veterinary clinicians.
  • To facilitate large-scale data analysis and translational research in veterinary medicine.

Main Methods:

  • Developed DeepTag, a deep learning algorithm utilizing a multitask LSTM architecture.
  • Trained DeepTag on a curated dataset of 112,558 expert-annotated veterinary notes.
  • Incorporated a hierarchical objective to capture semantic disease structures and a human-machine collaboration mechanism for uncertain cases.

Main Results:

  • DeepTag accurately infers disease codes from veterinary free-text notes.
  • The algorithm demonstrates strong performance even in cross-hospital settings.
  • Automated annotation is achieved across a broad range of diagnoses with minimal preprocessing.

Conclusions:

  • DeepTag effectively automates the diagnostic coding process in veterinary medicine.
  • The system enhances the utility of clinical records for patient care and research.
  • The underlying framework has potential applications in other medical domains lacking coding resources.