Clinical use of semantic space models in psychiatry and neurology: A systematic review and meta-analysis
Files
Publication date
2018-10-01
Authors
de Boer, J.N.
Voppel, A.E.
Begemann, M J H
Schnack, Hugo
Wijnen, F. N K
Sommer, I.E.C.
Editors
Advisors
Supervisors
Document Type
Article
Metadata
Show full item recordCollections
License
taverne
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
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, Taverne, SDG 3 - Good Health and Well-being
Citation
de Boer, J N, Voppel, A E, Begemann, M J H, Schnack, H G, Wijnen, F & Sommer, I E C 2018, 'Clinical use of semantic space models in psychiatry and neurology: A systematic review and meta-analysis', Neuroscience and Biobehavioral Reviews, vol. 93, pp. 85-92. https://doi.org/10.1016/j.neubiorev.2018.06.008