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

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    <subfield code="a">&lt;p&gt;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.&lt;/p&gt;</subfield>
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    <subfield code="a">Kizilay, Elif</subfield>
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    <subfield code="a">Eyuboglu, Merve Sumeyye</subfield>
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