High Predictive Accuracy of Negative Schizotypy With Acoustic Measures.

ABSTRACT: Negative schizotypal traits potentially can be digitally phenotyped using objective vocal analysis. Prior attempts have shown mixed success in this regard, potentially because acoustic analysis has relied on small, constrained feature sets. We employed machine learning to (a) optimize and cross-validate predictive models of self-reported negative schizotypy using a large acoustic feature set, (b) evaluate model performance as a function of sex and speaking task, (c) understand potential mechanisms underlying negative schizotypal traits by evaluating the key acoustic features within these models, and (d) examine model performance in its convergence with clinical symptoms and cognitive functioning. Accuracy was good (> 80%) and was improved by considering speaking task and sex. However, the

SUBMITTER: Cohen AS 

PROVIDER: S-EPMC10686546 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature