Strous Rael D, Koppel Moshe, Fine Jonathan, Nachliel Smadar, Shaked Ginette, Zivotofsky Ari Z
Beer Yaakov Mental Health Center, Beer Yaakov, Israel.
J Nerv Ment Dis. 2009 Aug;197(8):585-8. doi: 10.1097/NMD.0b013e3181b09068.
Prominent formal thought disorder, expressed as unusual language in speech and writing, is often a central feature of Schizophrenia. Since a more comprehensive understanding of phenomenology surrounding thought disorder is needed, this study investigates these processes by examining writing in Schizophrenia by novel computer-aided analysis. Thirty-six patients with DSM-IV criteria chronic Schizophrenia provided a page of writing (300-500 words) on a designated subject. Writing was examined by automated text categorization and compared with nonpsychiatrically ill individuals, investigating any differences with regards to lexical and syntactical features. Computerized methods used included extracting relevant text features, and utilizing machine learning techniques to induce mathematical models distinguishing between texts belonging to different categories. Observations indicated that automated methods distinguish schizophrenia writing with 83.3% accuracy. Results reflect underlying impaired processes including semantic deficit, independently establishing connection between primary pathology and language.
显著的形式思维障碍，表现为言语和写作中的异常语言，往往是精神分裂症的核心特征。由于需要更全面地理解围绕思维障碍的现象学，本研究通过新颖的计算机辅助分析检查精神分裂症患者的写作来调查这些过程。36名符合DSM-IV标准的慢性精神分裂症患者就指定主题提供了一页写作内容（300-500字）。通过自动文本分类对写作进行检查，并与非精神疾病患者进行比较，研究在词汇和句法特征方面的任何差异。使用的计算机化方法包括提取相关文本特征，以及利用机器学习技术来诱导区分属于不同类别的文本的数学模型。观察结果表明，自动化方法区分精神分裂症写作的准确率为83.3%。结果反映了包括语义缺陷在内的潜在受损过程，独立地在原发性病理和语言之间建立了联系。
