Schizophrenia<\/p>\n\n\n\n
Figueroa-Barra, A., Del Aguila, D., Cerda, M., Gaspar, P. A., Terissi, L. D., Dur\u00e1n, M., & Valderrama, C. (2022).<\/p>\n\n\n\n
ABSTRACT<\/strong>
Automated language analysis of speech has been shown to distinguish healthy control (HC) vs chronic schizophrenia (SZ) groups, yet the predictive power on first-episode psychosis patients (FEP) and the generalization to non-English speakers remain unclear. We performed a cross-sectional and longitudinal (18 months) automated language analysis in 133 Spanish-speaking subjects from three groups: healthy control or HC (n\u2009=\u200949), FEP (n\u2009=\u200940), and chronic SZ (n\u2009=\u200944). Interviews were manually transcribed, and the analysis included 30 language features (4 verbal fluency; 20 verbal productivity; 6 semantic coherence). Our cross-sectional analysis showed that using the top ten ranked and decorrelated language features, an automated HC vs SZ classification achieved 85.9% accuracy. In our longitudinal analysis, 28 FEP patients were diagnosed with SZ at the end of the study. Here, combining demographics, PANSS, and language information, the prediction accuracy reached 77.5% mainly driven by semantic coherence information. Overall, we showed that language features from Spanish-speaking clinical interviews can distinguish HC vs chronic SZ, and predict SZ diagnosis in FEP patients.<\/p>\n\n\n\n