Automated characterization and identification of schizophrenia in writing

Rael D. Strous, Moshe Koppel, Jonathan Fine, Smadar Nachliel, Ginette Shaked, Ari Z. Zivotofsky

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


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.

Original languageEnglish
Pages (from-to)585-588
Number of pages4
JournalJournal of Nervous and Mental Disease
Issue number8
StatePublished - Aug 2009


  • Automated text categorization
  • Schizophrenia
  • Thought disorder
  • Writing


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