Automated speech and language markers of longitudinal changes in psychosis symptoms

We sought to evaluate the ability of automated speech and language features to longitudinally track fluctuations in the major psychosis domains: <i>Thought Disorder</i>, <i>Negative Symptoms</i>, and <i>Positive Symptoms</i>. Sixty-six participants with psychotic disorders were assessed soon after inpatient admission, at discharge, and at 3- and 6-months. Psychosis symptoms were measured with semi-structured interviews and standardized scales. Recordings were collected from paragraph reading, fluency, picture description, and open-ended tasks. Relationships between psychosis symptoms and 357 automated speech and language features were analyzed using a single component score and as individual features, using linear mixed models. We found that all three domains demonstrated significant longitudinal relationships with the single component score. <i>Thought Disorder</i> was particularly related to features describing more subordinated constructions, less efficient identification of picture elements, and decreased semantic distance between sentences. <i>Negative Symptoms</i> was related to features describing decreased speech complexity. <i>Positive Symptoms</i> domain score did not show relationships with individual features that survived p-value correction, but <i>Suspiciousness</i> was related to decreased use of nouns and <i>Hallucinations</i> was related to greater semantic distances. These relationships were largely robust to interactions with gender and race. Interactions with timepoint revealed variable relationships during different phases of illness (acute vs. stable). In summary, automated speech and language features show promise as scalable, objective markers of psychosis severity. Detailed attention to clinical setting and patient population is needed to optimize clinical translation.