Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice

Abstract Cushing’s syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing’s syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predi...

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Autores principales:	Imogen Schofield, David C. Brodbelt, Noel Kennedy, Stijn J. M. Niessen, David B. Church, Rebecca F. Geddes, Dan G. O’Neill
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/aaa5c59b3d694511a04a7417cd900206
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spelling	oai:doaj.org-article:aaa5c59b3d694511a04a7417cd9002062021-12-02T17:39:19ZMachine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice10.1038/s41598-021-88440-z2045-2322https://doaj.org/article/aaa5c59b3d694511a04a7417cd9002062021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88440-zhttps://doaj.org/toc/2045-2322Abstract Cushing’s syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing’s syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predict a future diagnosis of Cushing's syndrome, using structured clinical data from the VetCompass programme in the UK. Dogs suspected of having Cushing's syndrome were included in the analysis and classified based on their final reported diagnosis within their clinical records. Demographic and clinical features available at the point of first suspicion by the attending veterinarian were included within the models. The machine-learning methods were able to classify the recorded Cushing’s syndrome diagnoses, with good predictive performance. The LASSO penalised regression model indicated the best overall performance when applied to the test set with an AUROC = 0.85 (95% CI 0.80–0.89), sensitivity = 0.71, specificity = 0.82, PPV = 0.75 and NPV = 0.78. The findings of our study indicate that machine-learning methods could predict the future diagnosis of a practicing veterinarian. New approaches using these methods could support clinical decision-making and contribute to improved diagnosis of Cushing’s syndrome in dogs.Imogen SchofieldDavid C. BrodbeltNoel KennedyStijn J. M. NiessenDavid B. ChurchRebecca F. GeddesDan G. O’NeillNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Imogen Schofield David C. Brodbelt Noel Kennedy Stijn J. M. Niessen David B. Church Rebecca F. Geddes Dan G. O’Neill Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice
description	Abstract Cushing’s syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing’s syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predict a future diagnosis of Cushing's syndrome, using structured clinical data from the VetCompass programme in the UK. Dogs suspected of having Cushing's syndrome were included in the analysis and classified based on their final reported diagnosis within their clinical records. Demographic and clinical features available at the point of first suspicion by the attending veterinarian were included within the models. The machine-learning methods were able to classify the recorded Cushing’s syndrome diagnoses, with good predictive performance. The LASSO penalised regression model indicated the best overall performance when applied to the test set with an AUROC = 0.85 (95% CI 0.80–0.89), sensitivity = 0.71, specificity = 0.82, PPV = 0.75 and NPV = 0.78. The findings of our study indicate that machine-learning methods could predict the future diagnosis of a practicing veterinarian. New approaches using these methods could support clinical decision-making and contribute to improved diagnosis of Cushing’s syndrome in dogs.
format	article
author	Imogen Schofield David C. Brodbelt Noel Kennedy Stijn J. M. Niessen David B. Church Rebecca F. Geddes Dan G. O’Neill
author_facet	Imogen Schofield David C. Brodbelt Noel Kennedy Stijn J. M. Niessen David B. Church Rebecca F. Geddes Dan G. O’Neill
author_sort	Imogen Schofield
title	Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice
title_short	Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice
title_full	Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice
title_fullStr	Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice
title_full_unstemmed	Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice
title_sort	machine-learning based prediction of cushing’s syndrome in dogs attending uk primary-care veterinary practice
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/aaa5c59b3d694511a04a7417cd900206
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Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice

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