![]() ![]() Implications for validating and implementing digital phenotyping to reduce SMI burden are discussed.īlunted vocal affect (BvA) and alogia, defined in terms of reduced vocal prosody and verbal production, respectively, are diagnostic criteria of schizophrenia 1 and are present in major depressive, post-traumatic, neurocognitive, and neurodegenerative spectrum disorders 2, 3, 4. However, the features identified as most predictive of BvA/Alogia were generally not considered critical to their operational definitions. ML scores were associated with poor cognitive performance and social functioning and were higher in patients with schizophrenia versus depression or mania diagnoses. ![]() ![]() Accuracy was high (>90%) and was improved when computed separately by speaking task. We sought to evaluate (1) whether clinically rated blunted vocal affect (BvA)/alogia could be accurately modelled using machine learning (ML) with a large feature set from two separate tasks (i.e., a 20-s “picture” and a 60-s “free-recall” task), (2) whether “Predicted” BvA/alogia (computed from the ML model) are associated with demographics, diagnosis, psychiatric symptoms, and cognitive/social functioning, and (3) which key vocal features are central to BvA/Alogia ratings. In prior studies, vocal measures show low convergence with clinical ratings, potentially because analysis has used small, constrained acoustic feature sets. Negative symptoms are a transdiagnostic feature of serious mental illness (SMI) that can be potentially “digitally phenotyped” using objective vocal analysis. ![]()
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