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

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. This study performed a cross-sectional and longitudinal (18 months) automated language analysis in 133 Spanish-speaking subjects from three groups: HC (n = 49), FEP (n = 40), and chronic SZ (n = 44). Using the top ten ranked and decorrelated language features, an automated HC vs SZ classification achieved 85.9% accuracy. In the longitudinal analysis, combining demographics, PANSS, and language information, the prediction accuracy of SZ diagnosis in FEP patients reached 77.5%, mainly driven by semantic coherence information. The study demonstrates that language features from Spanish-speaking clinical interviews can distinguish HC vs chronic SZ and predict SZ diagnosis in FEP patients.

Alicia Figueroa-Barra, Daniel Del Aguila, Mauricio Cerda, Pablo A. Gaspar, Lucas D. Terissi, Manuel Durán, Camila Valderrama