Changes in speech reflect changes in psychotic symptom severity: a longitudinal natural language processing analysis

The usefulness of automated speech and language markers to monitor or predict psychotic symptoms depends on their ability to detect changes in mental state. To date, research linking psychosis and Natural Language Processing (NLP) has been conducted almost exclusively using cross-sectional experimental designs, limiting insight into how intraindividual speech changes relate to symptoms and functioning. Therefore, here we examined the speech of 95 individuals diagnosed with schizophrenia ( n = 70) or schizoaffective disorder ( n = 25) in a longitudinal design. Speech samples (using the Narrative of Emotions Task) and clinical ratings (PANSS, MINI-ICF) were collected at baseline and again at six-month follow-up. We computed longitudinal changes in eight NLP markers reflecting speech quantity, lexical and syntactic complexity, semantic similarity and connectivity. Our results replicated established associations between NLP metrics and clinical symptoms at baseline, for example local semantic similarity between adjacent sentences was negatively correlated with the PANSS negative symptom scale. Longitudinal analyses showed that measures of moving average type-token ratio, total token count, semantic similarity and graph connectivity were most sensitive to intraindividual changes in symptom severity and functional impairment. Finally, partial least squares regression yielded a significant association between longitudinal changes in these metrics and a component reflecting change in total and negative symptoms. Overall, our findings support the potential of NLP methods to help monitor symptoms for individuals with psychotic disorders. By enabling the early detection of symptom worsening, NLP tools could enable more timely intervention to prevent more serious illness.