Classification of Paediatric Inflammatory Bowel Disease using Machine Learning

Abstract Paediatric inflammatory bowel disease (PIBD), comprising Crohn’s disease (CD), ulcerative colitis (UC) and inflammatory bowel disease unclassified (IBDU) is a complex and multifactorial condition with increasing incidence. An accurate diagnosis of PIBD is necessary for a prompt and effectiv...

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Autores principales:	E. Mossotto, J. J. Ashton, T. Coelho, R. M. Beattie, B. D. MacArthur, S. Ennis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8649a1f2a6dd4bae9d3b9ab85e0cb13f
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spelling	oai:doaj.org-article:8649a1f2a6dd4bae9d3b9ab85e0cb13f2021-12-02T15:05:35ZClassification of Paediatric Inflammatory Bowel Disease using Machine Learning10.1038/s41598-017-02606-22045-2322https://doaj.org/article/8649a1f2a6dd4bae9d3b9ab85e0cb13f2017-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-02606-2https://doaj.org/toc/2045-2322Abstract Paediatric inflammatory bowel disease (PIBD), comprising Crohn’s disease (CD), ulcerative colitis (UC) and inflammatory bowel disease unclassified (IBDU) is a complex and multifactorial condition with increasing incidence. An accurate diagnosis of PIBD is necessary for a prompt and effective treatment. This study utilises machine learning (ML) to classify disease using endoscopic and histological data for 287 children diagnosed with PIBD. Data were used to develop, train, test and validate a ML model to classify disease subtype. Unsupervised models revealed overlap of CD/UC with broad clustering but no clear subtype delineation, whereas hierarchical clustering identified four novel subgroups characterised by differing colonic involvement. Three supervised ML models were developed utilising endoscopic data only, histological only and combined endoscopic/histological data yielding classification accuracy of 71.0%, 76.9% and 82.7% respectively. The optimal combined model was tested on a statistically independent cohort of 48 PIBD patients from the same clinic, accurately classifying 83.3% of patients. This study employs mathematical modelling of endoscopic and histological data to aid diagnostic accuracy. While unsupervised modelling categorises patients into four subgroups, supervised approaches confirm the need of both endoscopic and histological evidence for an accurate diagnosis. Overall, this paper provides a blueprint for ML use with clinical data.E. MossottoJ. J. AshtonT. CoelhoR. M. BeattieB. D. MacArthurS. EnnisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q E. Mossotto J. J. Ashton T. Coelho R. M. Beattie B. D. MacArthur S. Ennis Classification of Paediatric Inflammatory Bowel Disease using Machine Learning
description	Abstract Paediatric inflammatory bowel disease (PIBD), comprising Crohn’s disease (CD), ulcerative colitis (UC) and inflammatory bowel disease unclassified (IBDU) is a complex and multifactorial condition with increasing incidence. An accurate diagnosis of PIBD is necessary for a prompt and effective treatment. This study utilises machine learning (ML) to classify disease using endoscopic and histological data for 287 children diagnosed with PIBD. Data were used to develop, train, test and validate a ML model to classify disease subtype. Unsupervised models revealed overlap of CD/UC with broad clustering but no clear subtype delineation, whereas hierarchical clustering identified four novel subgroups characterised by differing colonic involvement. Three supervised ML models were developed utilising endoscopic data only, histological only and combined endoscopic/histological data yielding classification accuracy of 71.0%, 76.9% and 82.7% respectively. The optimal combined model was tested on a statistically independent cohort of 48 PIBD patients from the same clinic, accurately classifying 83.3% of patients. This study employs mathematical modelling of endoscopic and histological data to aid diagnostic accuracy. While unsupervised modelling categorises patients into four subgroups, supervised approaches confirm the need of both endoscopic and histological evidence for an accurate diagnosis. Overall, this paper provides a blueprint for ML use with clinical data.
format	article
author	E. Mossotto J. J. Ashton T. Coelho R. M. Beattie B. D. MacArthur S. Ennis
author_facet	E. Mossotto J. J. Ashton T. Coelho R. M. Beattie B. D. MacArthur S. Ennis
author_sort	E. Mossotto
title	Classification of Paediatric Inflammatory Bowel Disease using Machine Learning
title_short	Classification of Paediatric Inflammatory Bowel Disease using Machine Learning
title_full	Classification of Paediatric Inflammatory Bowel Disease using Machine Learning
title_fullStr	Classification of Paediatric Inflammatory Bowel Disease using Machine Learning
title_full_unstemmed	Classification of Paediatric Inflammatory Bowel Disease using Machine Learning
title_sort	classification of paediatric inflammatory bowel disease using machine learning
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/8649a1f2a6dd4bae9d3b9ab85e0cb13f
work_keys_str_mv	AT emossotto classificationofpaediatricinflammatoryboweldiseaseusingmachinelearning AT jjashton classificationofpaediatricinflammatoryboweldiseaseusingmachinelearning AT tcoelho classificationofpaediatricinflammatoryboweldiseaseusingmachinelearning AT rmbeattie classificationofpaediatricinflammatoryboweldiseaseusingmachinelearning AT bdmacarthur classificationofpaediatricinflammatoryboweldiseaseusingmachinelearning AT sennis classificationofpaediatricinflammatoryboweldiseaseusingmachinelearning
_version_	1718388776869298176

Classification of Paediatric Inflammatory Bowel Disease using Machine Learning

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