Publication
Title
Machine learning in neurology : what neurologists can learn from machines and vice versa
Author
Abstract
Artificial intelligence is increasingly becoming a part of everyday life. This raises the question whether clinical neurology can benefit from these novel methods to increase diagnostic accuracy. Several recent studies have used machine learning classifiers to predict whether subjects suffer from a neurological disorder. This article discusses whether these methods are ready to make their entrance into clinical practice. The underlying principles of classification will be explored, as well as the potential pitfalls. Strengths of machine learning methods are that they are unbiased and very sensitive to patterns emerging from small changes spread across a large number of variables. Potential pitfalls are that building reliable classifiers requires large amounts of well-selected data and extensive validation. Currently, machine learning classifiers offer neurologists a new diagnostic tool which can aid in the diagnosis of cases with a high degree of uncertainty.
Language
English
Source (journal)
Journal of neurology. - Berlin, 1974, currens
Publication
Berlin : 2018
ISSN
0340-5354 [print]
1432-1459 [online]
Volume/pages
265 :11 (2018) , p. 2745-2748
ISI
000448450200030
Pubmed ID
30073503
Full text (Publisher's DOI)
Full text (open access)
Full text (publisher's version - intranet only)
UAntwerpen
Publication type
Subject
External links
Web of Science
Record
Identifier
Creation 17.08.2021
Last edited 23.12.2021