Cargando…

Automated prediction of mastitis infection patterns in dairy herds using machine learning

Mastitis in dairy cattle is extremely costly both in economic and welfare terms and is one of the most significant drivers of antimicrobial usage in dairy cattle. A critical step in the prevention of mastitis is the diagnosis of the predominant route of transmission of pathogens into either contagio...

Descripción completa

Detalles Bibliográficos
Autores principales: Hyde, Robert M., Down, Peter M., Bradley, Andrew J., Breen, James E., Hudson, Chris, Leach, Katharine A., Green, Martin J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062853/
https://www.ncbi.nlm.nih.gov/pubmed/32152401
http://dx.doi.org/10.1038/s41598-020-61126-8
_version_ 1783504594090852352
author Hyde, Robert M.
Down, Peter M.
Bradley, Andrew J.
Breen, James E.
Hudson, Chris
Leach, Katharine A.
Green, Martin J.
author_facet Hyde, Robert M.
Down, Peter M.
Bradley, Andrew J.
Breen, James E.
Hudson, Chris
Leach, Katharine A.
Green, Martin J.
author_sort Hyde, Robert M.
collection PubMed
description Mastitis in dairy cattle is extremely costly both in economic and welfare terms and is one of the most significant drivers of antimicrobial usage in dairy cattle. A critical step in the prevention of mastitis is the diagnosis of the predominant route of transmission of pathogens into either contagious (CONT) or environmental (ENV), with environmental being further subdivided as transmission during either the nonlactating “dry” period (EDP) or lactating period (EL). Using data from 1000 farms, random forest algorithms were able to replicate the complex herd level diagnoses made by specialist veterinary clinicians with a high degree of accuracy. An accuracy of 98%, positive predictive value (PPV) of 86% and negative predictive value (NPV) of 99% was achieved for the diagnosis of CONT vs ENV (with CONT as a “positive” diagnosis), and an accuracy of 78%, PPV of 76% and NPV of 81% for the diagnosis of EDP vs EL (with EDP as a “positive” diagnosis). An accurate, automated mastitis diagnosis tool has great potential to aid non-specialist veterinary clinicians to make a rapid herd level diagnosis and promptly implement appropriate control measures for an extremely damaging disease in terms of animal health, productivity, welfare and antimicrobial use.
format Online
Article
Text
id pubmed-7062853
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-70628532020-03-18 Automated prediction of mastitis infection patterns in dairy herds using machine learning Hyde, Robert M. Down, Peter M. Bradley, Andrew J. Breen, James E. Hudson, Chris Leach, Katharine A. Green, Martin J. Sci Rep Article Mastitis in dairy cattle is extremely costly both in economic and welfare terms and is one of the most significant drivers of antimicrobial usage in dairy cattle. A critical step in the prevention of mastitis is the diagnosis of the predominant route of transmission of pathogens into either contagious (CONT) or environmental (ENV), with environmental being further subdivided as transmission during either the nonlactating “dry” period (EDP) or lactating period (EL). Using data from 1000 farms, random forest algorithms were able to replicate the complex herd level diagnoses made by specialist veterinary clinicians with a high degree of accuracy. An accuracy of 98%, positive predictive value (PPV) of 86% and negative predictive value (NPV) of 99% was achieved for the diagnosis of CONT vs ENV (with CONT as a “positive” diagnosis), and an accuracy of 78%, PPV of 76% and NPV of 81% for the diagnosis of EDP vs EL (with EDP as a “positive” diagnosis). An accurate, automated mastitis diagnosis tool has great potential to aid non-specialist veterinary clinicians to make a rapid herd level diagnosis and promptly implement appropriate control measures for an extremely damaging disease in terms of animal health, productivity, welfare and antimicrobial use. Nature Publishing Group UK 2020-03-09 /pmc/articles/PMC7062853/ /pubmed/32152401 http://dx.doi.org/10.1038/s41598-020-61126-8 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Hyde, Robert M.
Down, Peter M.
Bradley, Andrew J.
Breen, James E.
Hudson, Chris
Leach, Katharine A.
Green, Martin J.
Automated prediction of mastitis infection patterns in dairy herds using machine learning
title Automated prediction of mastitis infection patterns in dairy herds using machine learning
title_full Automated prediction of mastitis infection patterns in dairy herds using machine learning
title_fullStr Automated prediction of mastitis infection patterns in dairy herds using machine learning
title_full_unstemmed Automated prediction of mastitis infection patterns in dairy herds using machine learning
title_short Automated prediction of mastitis infection patterns in dairy herds using machine learning
title_sort automated prediction of mastitis infection patterns in dairy herds using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062853/
https://www.ncbi.nlm.nih.gov/pubmed/32152401
http://dx.doi.org/10.1038/s41598-020-61126-8
work_keys_str_mv AT hyderobertm automatedpredictionofmastitisinfectionpatternsindairyherdsusingmachinelearning
AT downpeterm automatedpredictionofmastitisinfectionpatternsindairyherdsusingmachinelearning
AT bradleyandrewj automatedpredictionofmastitisinfectionpatternsindairyherdsusingmachinelearning
AT breenjamese automatedpredictionofmastitisinfectionpatternsindairyherdsusingmachinelearning
AT hudsonchris automatedpredictionofmastitisinfectionpatternsindairyherdsusingmachinelearning
AT leachkatharinea automatedpredictionofmastitisinfectionpatternsindairyherdsusingmachinelearning
AT greenmartinj automatedpredictionofmastitisinfectionpatternsindairyherdsusingmachinelearning