精神分裂症的连贯性模型

S. Just, Erik Haegert, Nora Kořánová, Anna-Lena Bröcker, Ivan Nenchev, J. Funcke, C. Montag, Manfred Stede
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引用次数: 17

摘要

精神分裂症的语无伦次一直被认为是精神障碍的主要症状(Bleuler, 1911/1950)。最近的研究使用现代句子和词嵌入来计算精神分裂症患者自发言语的连贯度量。虽然临床评分总是有主观因素,但计算语言学方法允许对言语异常进行量化。来自精神病学的临床和经验知识为建模提供了理论和概念基础。我们的研究是一项跨学科的尝试,旨在改善精神分裂症的连贯模型。语言样本来自健康对照和诊断为精神分裂症或分裂情感性障碍和不同严重程度的积极形式思维障碍的患者。对访谈进行转录,并从不同的嵌入中得出一致性指标。一个模型发现对照组的一致性指标高于患者。所有其他模型仍然不显著。更详细的数据分析激发了不同的方法来改善精神分裂症的一致性模型,例如通过评估参照异常。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Coherence models in schizophrenia
Incoherent discourse in schizophrenia has long been recognized as a dominant symptom of the mental disorder (Bleuler, 1911/1950). Recent studies have used modern sentence and word embeddings to compute coherence metrics for spontaneous speech in schizophrenia. While clinical ratings always have a subjective element, computational linguistic methodology allows quantification of speech abnormalities. Clinical and empirical knowledge from psychiatry provide the theoretical and conceptual basis for modelling. Our study is an interdisciplinary attempt at improving coherence models in schizophrenia. Speech samples were obtained from healthy controls and patients with a diagnosis of schizophrenia or schizoaffective disorder and different severity of positive formal thought disorder. Interviews were transcribed and coherence metrics derived from different embeddings. One model found higher coherence metrics for controls than patients. All other models remained non-significant. More detailed analysis of the data motivates different approaches to improving coherence models in schizophrenia, e.g. by assessing referential abnormalities.
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