Evidence map›Paper›PMID 41039394›Full record

ArticleBMC veterinary research2025

Anna: an open-source platform for real-time integration of machine learning classifiers with veterinary electronic health records.

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC veterinary research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Chun Yin KongDepartment of Pathology, Microbiology, Immunology, University of California Davis, Davis, CA, USA.ORCID http://orcid.org/0009-0008-7731-9294
Picasso VasquezIndependent Researcher, San Francisco, CA, USA.
Makan FarhoodimoghadamDepartment of Computer Science, University of California Davis, Davis, CA, USA.
Chris BrandtSchool of Veterinary Medicine Information Technology, University of California Davis, Davis, CA, USA.ORCID http://orcid.org/0009-0005-0862-3629
Titus C BrownDepartment of Population Health & Reproduction, University of California Davis, Davis, CA, USA.ORCID http://orcid.org/0000-0001-6001-2677
Krystle L ReaganDepartment of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, 80523, USA.ORCID http://orcid.org/0000-0003-3426-6352
Allison ZwingenbergerDepartment of Surgical & Radiological Sciences, University of California Davis, Davis, CA, USA.ORCID http://orcid.org/0000-0002-8982-2558
Stefan M KellerDepartment of Pathology, Microbiology, Immunology, University of California Davis, Davis, CA, USA. smkeller@ucdavis.edu.ORCID http://orcid.org/0000-0002-5428-2985

Funding

Center for Companion Animal Health, University of California, Davis 2021-24-FUC Davis Center for Equine Health 21-Y
6 · The paper itself

Abstract

backgroundIn 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 EHR systems in veterinary medicine is often hindered by the inherent rigidity of these systems or by the limited availability of IT resources to implement the modifications necessary for ML compatibility.

resultsAnna is a standalone analytics platform that can host ML classifiers and interfaces with EHR systems to provide classifier predictions for laboratory data in real-time. Following a request from the EHR system, Anna retrieves patient-specific data from the EHR system, merges diagnostic test results based on user-defined temporal criteria and returns predictions for all available classifiers for display in real-time. Anna was developed in Python and is freely available. Because Anna is a stand-alone platform, it does not require substantial modifications to the existing EHR, allowing for easy integration into existing computing infrastructure. To demonstrate Anna's versatility, we implemented three previously published ML classifiers to predict a diagnosis of hypoadrenocorticism, leptospirosis, or a portosystemic shunt in dogs.

conclusionAnna is an open-source tool designed to improve the accessibility of ML classifiers for the veterinary community. Its flexible architecture supports the integration of classifiers developed in various programming languages and with diverse environment requirements. Anna facilitates rapid prototyping, enabling researchers and developers to deploy ML classifiers quickly without modifications to the existing EHR system. Anna could drive broader adoption of ML in veterinary practices, ultimately enhancing diagnostic capabilities and patient outcomes.

Indexed as

Electronic Health RecordsMachine LearningSoftwareVeterinary MedicineAnimalsDog DiseasesDogsArtificial intelligenceClinical decision-making supportElectronic health recordsHypoadrenocorticismLeptospirosisMachine learningMachine learning classifiersMachine learning integrationPortosystemic shuntReal-time data analysisVeterinary medicine

Identifiers

PMID41039394
PMCPMC12492907

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.