Clinical use of semantic space models in psychiatry and neurology: A systematic review and meta-analysis

J.N. de Boer; A.E. Voppel; M.J.H. Begemann; H.G. Schnack; F. Wijnen; I.E.C. Sommer

doi:10.1016/j.neubiorev.2018.06.008

Clinical use of semantic space models in psychiatry and neurology: A systematic review and meta-analysis

J.N. de Boer, A.E. Voppel, M.J.H. Begemann, H.G. Schnack, F. Wijnen, I.E.C. Sommer

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Verbal communication disorders are a hallmark of many neurological and psychiatric illnesses. Recent developments in computational analysis provide objective characterizations of these language abnormalities. We conducted a meta-analysis assessing semantic space models as a diagnostic or prognostic tool in psychiatric or neurological disorders. Diagnostic test accuracy analyses revealed reasonable sensitivity and specificity and high overall efficacy in differentiating between patients and controls (n=1680: Hedges’ g =.73, p=.001). Analyses of full sentences (Hedges’ g =.95 p

Original language	English
Pages (from-to)	85-92
Number of pages	8
Journal	Neuroscience and Biobehavioral Reviews
Volume	93
DOIs	https://doi.org/10.1016/j.neubiorev.2018.06.008
Publication status	Published - 1 Oct 2018

Keywords

Natural language processing
Neurology
Psychiatry
Semantic space
Vector space
attention deficit disorder
autism
dementia
diagnostic accuracy
differential diagnosis
human
mental disease
meta analysis
neurologic disease
Parkinson disease
priority journal
psychosis
receiver operating characteristic
review
semantic space model
semantics
sensitivity and specificity
systematic review
verbal communication

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.neubiorev.2018.06.008

deboerFinal published version, 693 KBLicence: Taverne

Cite this

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AU - Wijnen, F.

AU - Sommer, I.E.C.

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KW - mental disease

KW - meta analysis

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Clinical use of semantic space models in psychiatry and neurology: A systematic review and meta-analysis

Abstract

Keywords

UN SDGs

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