Saved in:
Bibliographic Details
Main Authors: Kong, Chun Yin, Vasquez, Picasso, Farhoodimoghadam, Makan, Brandt, Chris, Brown, Titus C., Reagan, Krystle L., Zwingenberger, Allison, Keller, Stefan M.
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2410.14625
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866910656915046400
author Kong, Chun Yin
Vasquez, Picasso
Farhoodimoghadam, Makan
Brandt, Chris
Brown, Titus C.
Reagan, Krystle L.
Zwingenberger, Allison
Keller, Stefan M.
author_facet Kong, Chun Yin
Vasquez, Picasso
Farhoodimoghadam, Makan
Brandt, Chris
Brown, Titus C.
Reagan, Krystle L.
Zwingenberger, Allison
Keller, Stefan M.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2410_14625
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Enhancing AI Accessibility in Veterinary Medicine: Linking Classifiers and Electronic Health Records
Kong, Chun Yin
Vasquez, Picasso
Farhoodimoghadam, Makan
Brandt, Chris
Brown, Titus C.
Reagan, Krystle L.
Zwingenberger, Allison
Keller, Stefan M.
Information Retrieval
Machine Learning
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.
title Enhancing AI Accessibility in Veterinary Medicine: Linking Classifiers and Electronic Health Records
topic Information Retrieval
Machine Learning
url https://arxiv.org/abs/2410.14625