Leveraging computational linguistics and machine learning for detection of ultra-high risk of mental health disorders in youths.

Mental illnesses often manifest through behavioral changes, with speech serving as a key medium for expressing thoughts and emotions. The use of computational linguistics on speech data in mental illnesses is a promising approach to uncover objective biomarkers for the early detection of mental illnesses. This study analyzed speech transcripts from 80 youths at ultra-high risk of psychosis (UHR) and 329 healthy controls, examining text features such as sentiment variability, cohesion, lexical sophistication, morphology, syntactic sophistication, and lexical diversity.