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


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Abstract

HighlightsSemantic space models are a valuable diagnostic tool in various brain disorders.Analyzing full sentences is more effective than analyzing single words.Models examining psychosis or autism patients were most effective.These models are mostly used compare patients to healthy controls.Future studies should test these models' specificity between different disorders.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 <.0001) revealed a higher efficacy than single words (Hedges' g = .51, p <.0001). Specifically, models examining psychotic patients (Hedges' g =.96, p=.003) and those with autism (Hedges' g = .84, p <.0001) were highly effective. Our results show semantic space models are effective as a diagnostic tool in a variety of psychiatric and neurological disorders. The field is still exploratory in nature; techniques differ and models are only used to distinguish patients from healthy controls so far. Future research should aim to distinguish between disorders and perhaps explore newer semantic space tools like word2vec.

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