The diagnosis and treatment of psychiatric disorders depends on the analysis of behavior through language by a clinical specialist. This analysis is subjective in nature and could benefit from automated, objective acoustic and linguistic processing methods. This integrated approach would convey a richer representation of patient speech, particularly for expression of emotion. In this work, we explore the potential of acoustic and prosodic metrics to infer clinical variables and predict psychosis, a condition which produces measurable derailment and tangentiality in patient language. To that purpose, we analyzed the recordings of 32 young patients at high risk of developing clinical psychosis. The subjects were evaluated using the Structured Interview for Prodromal Syndromes/Scale of Prodromal Symptoms (SIPS/SOPS) criteria. To analyze the recordings, we examined the variation of different acoustic and prosodic metrics across time. This preliminary analysis shows that these features can infer negative symptom severity ratings (i.e., SIPS-Btotal), obtaining a Pearson correlation of 0.77 for all the subjects after cross-validated evaluation. In addition, these features can predict development of psychosis with high accuracy above 90%, outperforming classification using clinical variables only. This improved predictive power ultimately can help provide early treatment and improve quality of life for those at risk for developing psychosis.