Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review.

Buchlak, QD; Esmaili, N; Bennett, C; Farrokhi, F

Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review.

Buchlak, QD Esmaili, N Bennett, C Farrokhi, F

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Publisher:: Springer
Publication Type:: Chapter
Citation:: Acta Neurochirurgica, Supplementum, 2022, 134, pp. 277-289
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Buchlak, QD
dc.contributor.author	Esmaili, N
dc.contributor.author	Bennett, C
dc.contributor.author	Farrokhi, F
dc.date.accessioned	2023-07-06T20:16:04Z
dc.date.available	2023-07-06T20:16:04Z
dc.date.issued	2022
dc.identifier.citation	Acta Neurochirurgica, Supplementum, 2022, 134, pp. 277-289
dc.identifier.isbn	978-3-030-85294-8
dc.identifier.uri	http://hdl.handle.net/10453/171279
dc.description.abstract	Natural language processing (NLP), a domain of artificial intelligence (AI) that models human language, has been used in medicine to automate diagnostics, detect adverse events, support decision making and predict clinical outcomes. However, applications to the clinical neurosciences appear to be limited. NLP has matured with the implementation of deep transformer models (e.g., XLNet, BERT, T5, and RoBERTa) and transfer learning. The objectives of this study were to (1) systematically review NLP applications in the clinical neurosciences, and (2) explore NLP analysis to facilitate literature synthesis, providing clear examples to demonstrate the potential capabilities of these technologies for a clinical audience. Our NLP analysis consisted of keyword identification, text summarization and document classification. A total of 48 articles met inclusion criteria. NLP has been applied in the clinical neurosciences to facilitate literature synthesis, data extraction, patient identification, automated clinical reporting and outcome prediction. The number of publications applying NLP has increased rapidly over the past five years. Document classifiers trained to differentiate included and excluded articles demonstrated moderate performance (XLNet AUC = 0.66, BERT AUC = 0.59, RoBERTa AUC = 0.62). The T5 transformer model generated acceptable abstract summaries. The application of NLP has the potential to enhance research and practice in the clinical neurosciences.
dc.format	Print
dc.format.extent	49
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Acta Neurochirurgica, Supplementum
dc.relation.ispartofseries	Acta Neurochirurgica Supplement
dc.relation.isbasedon	10.1007/978-3-030-85292-4_32
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1103 Clinical Sciences, 1109 Neurosciences
dc.subject.classification	Neurology & Neurosurgery
dc.subject.classification	3202 Clinical sciences
dc.subject.classification	3203 Dentistry
dc.subject.classification	3209 Neurosciences
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Humans
dc.subject.mesh	Machine Learning
dc.subject.mesh	Natural Language Processing
dc.subject.mesh	Neurosciences
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Humans
dc.subject.mesh	Machine Learning
dc.subject.mesh	Natural Language Processing
dc.subject.mesh	Neurosciences
dc.subject.mesh	Humans
dc.subject.mesh	Neurosciences
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Natural Language Processing
dc.subject.mesh	Machine Learning
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Humans
dc.subject.mesh	Machine Learning
dc.subject.mesh	Natural Language Processing
dc.subject.mesh	Neurosciences
dc.title	Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review.
dc.type	Chapter
utslib.citation.volume	134
utslib.for	1103 Clinical Sciences
utslib.for	1109 Neurosciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-07-06T20:16:03Z
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.volume	134
dc.location	Switzerland

Abstract:

Natural language processing (NLP), a domain of artificial intelligence (AI) that models human language, has been used in medicine to automate diagnostics, detect adverse events, support decision making and predict clinical outcomes. However, applications to the clinical neurosciences appear to be limited. NLP has matured with the implementation of deep transformer models (e.g., XLNet, BERT, T5, and RoBERTa) and transfer learning. The objectives of this study were to (1) systematically review NLP applications in the clinical neurosciences, and (2) explore NLP analysis to facilitate literature synthesis, providing clear examples to demonstrate the potential capabilities of these technologies for a clinical audience. Our NLP analysis consisted of keyword identification, text summarization and document classification. A total of 48 articles met inclusion criteria. NLP has been applied in the clinical neurosciences to facilitate literature synthesis, data extraction, patient identification, automated clinical reporting and outcome prediction. The number of publications applying NLP has increased rapidly over the past five years. Document classifiers trained to differentiate included and excluded articles demonstrated moderate performance (XLNet AUC = 0.66, BERT AUC = 0.59, RoBERTa AUC = 0.62). The T5 transformer model generated acceptable abstract summaries. The application of NLP has the potential to enhance research and practice in the clinical neurosciences.

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http://hdl.handle.net/10453/171279