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Automatic language analysis identifies and predicts schizophrenia in first-episode of psychosis.

ABSTRACT: 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 = 49), FEP (n = 40), and chronic SZ (n = 44). 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.

SUBMITTER: Figueroa-Barra A

PROVIDER: S-EPMC9261086 | biostudies-literature |

REPOSITORIES: biostudies-literature

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