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In Schizophrenia bulletin ; h5-index 79.0

BACKGROUND AND HYPOTHESIS : Automated language analysis is becoming an increasingly popular tool in clinical research involving individuals with mental health disorders. Previous work has largely focused on using high-dimensional language features to develop diagnostic and prognostic models, but less work has been done to use linguistic output to assess downstream functional outcomes, which is critically important for clinical care. In this work, we study the relationship between automated language composites and clinical variables that characterize mental health status and functional competency using predictive modeling.

STUDY DESIGN : Conversational transcripts were collected from a social skills assessment of individuals with schizophrenia (n = 141), bipolar disorder (n = 140), and healthy controls (n = 22). A set of composite language features based on a theoretical framework of speech production were extracted from each transcript and predictive models were trained. The prediction targets included clinical variables for assessment of mental health status and social and functional competency. All models were validated on a held-out test sample not accessible to the model designer.

STUDY RESULTS : Our models predicted the neurocognitive composite with Pearson correlation PCC = 0.674; PANSS-positive with PCC = 0.509; PANSS-negative with PCC = 0.767; social skills composite with PCC = 0.785; functional competency composite with PCC = 0.616. Language features related to volition, affect, semantic coherence, appropriateness of response, and lexical diversity were useful for prediction of clinical variables.

CONCLUSIONS : Language samples provide useful information for the prediction of a variety of clinical variables that characterize mental health status and functional competency.

Voleti Rohit, Woolridge Stephanie M, Liss Julie M, Milanovic Melissa, Stegmann Gabriela, Hahn Shira, Harvey Philip D, Patterson Thomas L, Bowie Christopher R, Berisha Visar


bipolar disorder, machine learning, natural language processing, schizophrenia, social skills prediction, speech analysis