Anna: an open-source platform for real-time integration of machine learning classifiers with veterinary electronic health records.
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| Název: | Anna: an open-source platform for real-time integration of machine learning classifiers with veterinary electronic health records. |
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| Autoři: | Kong, Chun Yin1 (AUTHOR) cykkong@ucdavis.edu, Vasquez, Picasso2 (AUTHOR) picassovasquez@gmail.com, Farhoodimoghadam, Makan3 (AUTHOR) mfarhoodi@ucdavis.edu, Brandt, Chris4 (AUTHOR) cmbrandt@ucdavis.edu, Brown, Titus C.5 (AUTHOR) ctbrown@ucdavis.edu, Reagan, Krystle L.6 (AUTHOR) krystle.reagan@colostate.edu, Zwingenberger, Allison7 (AUTHOR) azwingen@ucdavis.edu, Keller, Stefan M.1 (AUTHOR) smkeller@ucdavis.edu |
| Zdroj: | BMC Veterinary Research. 10/2/2025, Vol. 21 Issue 1, p1-12. 12p. |
| Druh dokumentu: | Article |
| Témata: | Machine learning, Electronic health records, Open source software, Patient care, Veterinary medicine, Diagnosis, Classification algorithms, Synchronization |
| Author-Supplied Keywords: | Artificial intelligence Clinical decision-making support Hypoadrenocorticism Information and Computing Sciences Artificial Intelligence and Image Processing Computer Software Information Systems Leptospirosis Machine learning classifiers Machine learning integration Portosystemic shunt Real-time data analysis |
| Abstrakt: | Background: 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 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. Results: Anna 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. Conclusion: Anna 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. [ABSTRACT FROM AUTHOR] |
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| Author Affiliations: | 1https://ror.org/05rrcem69 Department of Pathology, Microbiology, Immunology, University of California Davis, Davis, CA, USA 2Independent Researcher, San Francisco, CA, USA 3https://ror.org/05rrcem69 Department of Computer Science, University of California Davis, Davis, CA, USA 4https://ror.org/05rrcem69 School of Veterinary Medicine Information Technology, University of California Davis, Davis, CA, USA 5https://ror.org/05rrcem69 Department of Population Health & Reproduction, University of California Davis, Davis, CA, USA 6https://ror.org/03k1gpj17 Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, 80523, Fort Collins, CO, USA 7https://ror.org/05rrcem69 Department of Surgical & Radiological Sciences, University of California Davis, Davis, CA, USA |
| Full Text Word Count: | 6701 |
| ISSN: | 1746-6148 |
| DOI: | 10.1186/s12917-025-05000-7 |
| Přístupové číslo: | 188451289 |
| Databáze: | Veterinary Source |
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