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.

言语交流障碍是许多神经系统疾病和精神疾病的标志。计算分析的最新发展为这些语言异常提供了客观的表征。我们进行了荟萃分析，评估了语义空间模型作为精神疾病或神经疾病的诊断或预后工具。诊断试验准确度分析显示在（患者和对照之间进行区分合理的灵敏度和特异性和高总体疗效= N 1680：套期克= 0.73，P = 0.001）。完整的句子的分析（套期克= 0.95 p <0.0001）显示比单字的较高效力（树篱克= 0.51，p<.0001）。具体地，模型检查精神病患者（树篱克= 0.96，P = 0.003）和那些患有孤独症（树篱克= 0.84，p <0.0001）是高度有效的。我们的结果表明，语义空间模型可以有效地作为各种精神病和神经病的诊断工具。该领域本质上仍是探索性的；迄今为止，技术不同，模型仅用于区分患者与健康对照。未来的研究应旨在区分疾病，并可能探索诸如word2vec之类的较新的语义空间工具。