The University of Southampton
University of Southampton Institutional Repository
Warning ePrints Soton is experiencing an issue with some file downloads not being available. We are working hard to fix this. Please bear with us.

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

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases
A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases
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studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion.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.
2398-6352
1-11
Stafford, I.S.
50987dc1-3772-408f-9093-9124f3d6b2cd
Kellermann, M.
e4cc843f-d5a5-4ec2-be22-1c83c9a46102
Mossotto, E.
a2a572db-3e95-41c6-94f6-f1b019594372
Beattie, R.M.
9a66af0b-f81c-485c-b01d-519403f0038a
MacArthur, B.D.
2c0476e7-5d3e-4064-81bb-104e8e88bb6b
Ennis, S.
7b57f188-9d91-4beb-b217-09856146f1e9
Stafford, I.S.
50987dc1-3772-408f-9093-9124f3d6b2cd
Kellermann, M.
e4cc843f-d5a5-4ec2-be22-1c83c9a46102
Mossotto, E.
a2a572db-3e95-41c6-94f6-f1b019594372
Beattie, R.M.
9a66af0b-f81c-485c-b01d-519403f0038a
MacArthur, B.D.
2c0476e7-5d3e-4064-81bb-104e8e88bb6b
Ennis, S.
7b57f188-9d91-4beb-b217-09856146f1e9

Stafford, I.S., Kellermann, M., Mossotto, E., Beattie, R.M., MacArthur, B.D. and Ennis, S. (2020) A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases. npj Digital Medicine, 3 (1), 1-11, [30]. (doi:10.1038/s41746-020-0229-3).

Record type: Article

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.

Text
Systematic Review AI and ML in Autoimmune Disease FINAL DRAFT - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (51kB)
Text
s41746-020-0229-3 - Version of Record
Available under License Creative Commons Attribution.
Download (939kB)

More information

Accepted/In Press date: 6 January 2020
Published date: 9 March 2020

Identifiers

Local EPrints ID: 438409
URI: http://eprints.soton.ac.uk/id/eprint/438409
ISSN: 2398-6352
PURE UUID: 22da0a87-40e2-4838-8185-5d67e9be99fb
ORCID for S. Ennis: ORCID iD orcid.org/0000-0003-2648-0869

Catalogue record

Date deposited: 09 Mar 2020 17:32
Last modified: 22 Nov 2021 02:44

Export record

Altmetrics

Contributors

Author: I.S. Stafford
Author: M. Kellermann
Author: E. Mossotto
Author: R.M. Beattie
Author: B.D. MacArthur
Author: S. Ennis ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×