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arXiv Open Access 2024

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

Chun Yin Kong Picasso Vasquez Makan Farhoodimoghadam Chris Brandt Titus C. Brown +3 lainnya
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Abstrak

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.

Topik & Kata Kunci

cs.IR cs.LG

Penulis (8)

C

Chun Yin Kong

P

Picasso Vasquez

M

Makan Farhoodimoghadam

C

Chris Brandt

T

Titus C. Brown

K

Krystle L. Reagan

A

Allison Zwingenberger

S

Stefan M. Keller

Format Sitasi

APA MLA BibTeX
Kong, C.Y., Vasquez, P., Farhoodimoghadam, M., Brandt, C., Brown, T.C., Reagan, K.L. et al. (2024). Enhancing AI Accessibility in Veterinary Medicine: Linking Classifiers and Electronic Health Records. https://arxiv.org/abs/2410.14625

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Informasi Jurnal
Tahun Terbit
2024
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓