1 Ocak 2024 Dergi makalesi Açık Erişim

Kizilay, Elif; Arslan, Berat; Verim, Burcu; Demirlek, Cemal; Demir, Muhammed; Cesim, Ezgi; Eyuboglu, Merve Sumeyye; Ozbek, Simge Uzman; Sut, Ekin; Yalincetin, Berna; Bora, Emre

Dublin Core

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Kizilay, Elif</dc:creator>
  <dc:creator>Arslan, Berat</dc:creator>
  <dc:creator>Verim, Burcu</dc:creator>
  <dc:creator>Demirlek, Cemal</dc:creator>
  <dc:creator>Demir, Muhammed</dc:creator>
  <dc:creator>Cesim, Ezgi</dc:creator>
  <dc:creator>Eyuboglu, Merve Sumeyye</dc:creator>
  <dc:creator>Ozbek, Simge Uzman</dc:creator>
  <dc:creator>Sut, Ekin</dc:creator>
  <dc:creator>Yalincetin, Berna</dc:creator>
  <dc:creator>Bora, Emre</dc:creator>
  <dc:date>2024-01-01</dc:date>
  <dc:description>Identifying individuals at clinical high risk for psychosis (CHR-P) - P) is crucial for preventing psychosis and improving the prognosis for schizophrenia. Individuals at CHR-P may exhibit mild forms of formal thought disorder (FTD), making it possible to identify them using natural language processing (NLP) methods. In this study, speech samples of 62 CHR-P individuals and 45 healthy controls (HCs) were elicited using Thematic Apperception Test images. The evaluation involved various NLP measures such as semantic similarity, generic, and part-of-speech (POS) features. The CHR-P group demonstrated higher sentence-level semantic similarity and reduced mean image-to-text similarity. Regarding generic analysis, they demonstrated reduced verbosity and produced shorter sentences with shorter words. The POS analysis revealed a decrease in the utilization of adverbs, conjunctions, and first-person singular pronouns, alongside an increase in the utilization of adjectives in the CHR-P group compared to HC. In addition, we developed a machine-learning model based on 30 NLP-derived features to distinguish between the CHR-P and HC groups. The model demonstrated an accuracy of 79.6 % and an AUC-ROC of 0.86. Overall, these findings suggest that automated language analysis of speech could provide valuable information for characterizing FTD during the clinical high-risk phase and has the potential to be applied objectively for early intervention for psychosis.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/276221</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:276221</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>SCHIZOPHRENIA RESEARCH 274 8</dc:source>
  <dc:title>Automated linguistic analysis in youth at clinical high risk for psychosis</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>