Enhancing AI Accessibility in Veterinary Medicine: Linking Classifiers and Electronic Health Records

Chun Yin Kong; Picasso Vasquez; Makan Farhoodimoghadam; Chris Brandt; Titus C. Brown; Krystle L. Reagan; Allison Zwingenberger; Stefan M. Keller

Enhancing AI Accessibility in Veterinary Medicine: Linking Classifiers and Electronic Health Records

Information Retrieval 2024-10-21 v1 Machine Learning

Authors: Chun Yin Kong , Picasso Vasquez , Makan Farhoodimoghadam , Chris Brandt , Titus C. Brown , Krystle L. Reagan , Allison Zwingenberger , Stefan M. Keller

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Abstract

In the rapidly evolving landscape of veterinary healthcare, integrating machine learning (ML) clinical decision-making tools with electronic health records (EHRs) promises to improve diagnostic accuracy and patient care. However, the seamless integration of ML classifiers into existing EHRs in veterinary medicine is frequently hindered by the rigidity of EHR systems or the limited availability of IT resources. To address this shortcoming, we present Anna, a freely-available software solution that provides ML classifier results for EHR laboratory data in real-time.

Keywords

machine learning veterinary medicine electronic health records clinical decision support AI accessibility

Cite

@article{arxiv.2410.14625,
  title  = {Enhancing AI Accessibility in Veterinary Medicine: Linking Classifiers and Electronic Health Records},
  author = {Chun Yin Kong and Picasso Vasquez and Makan Farhoodimoghadam and Chris Brandt and Titus C. Brown and Krystle L. Reagan and Allison Zwingenberger and Stefan M. Keller},
  journal= {arXiv preprint arXiv:2410.14625},
  year   = {2024}
}

Enhancing AI Accessibility in Veterinary Medicine: Linking Classifiers and Electronic Health Records

Abstract

Keywords

Cite

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