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

October 18, 2024 Β· Declared Dead Β· πŸ› arXiv.org

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Authors Chun Yin Kong, Picasso Vasquez, Makan Farhoodimoghadam, Chris Brandt, Titus C. Brown, Krystle L. Reagan, Allison Zwingenberger, Stefan M. Keller arXiv ID 2410.14625 Category cs.IR: Information Retrieval Cross-listed cs.LG Citations 0 Venue arXiv.org Last Checked 4 months ago
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.
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