A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Abstract Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management....

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Autores principales:	I. S. Stafford, M. Kellermann, E. Mossotto, R. M. Beattie, B. D. MacArthur, S. Ennis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/e4026e71a4cd4b3d84a2b91f2a05903b
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spelling	oai:doaj.org-article:e4026e71a4cd4b3d84a2b91f2a05903b2021-12-02T11:35:41ZA systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases10.1038/s41746-020-0229-32398-6352https://doaj.org/article/e4026e71a4cd4b3d84a2b91f2a05903b2020-03-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0229-3https://doaj.org/toc/2398-6352Abstract Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included “machine learning” or “artificial intelligence” and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.I. S. StaffordM. KellermannE. MossottoR. M. BeattieB. D. MacArthurS. EnnisNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 I. S. Stafford M. Kellermann E. Mossotto R. M. Beattie B. D. MacArthur S. Ennis A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases
description	Abstract Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included “machine learning” or “artificial intelligence” and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.
format	article
author	I. S. Stafford M. Kellermann E. Mossotto R. M. Beattie B. D. MacArthur S. Ennis
author_facet	I. S. Stafford M. Kellermann E. Mossotto R. M. Beattie B. D. MacArthur S. Ennis
author_sort	I. S. Stafford
title	A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases
title_short	A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases
title_full	A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases
title_fullStr	A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases
title_full_unstemmed	A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases
title_sort	systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/e4026e71a4cd4b3d84a2b91f2a05903b
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A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

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