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Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia

Negative symptoms are a transdiagnostic feature of serious mental illness (SMI) that can be potentially “digitally phenotyped” using objective vocal analysis. In prior studies, vocal measures show low convergence with clinical ratings, potentially because analysis has used small, constrained acousti...

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Autores principales: Cohen, Alex S., Cox, Christopher R., Le, Thanh P., Cowan, Tovah, Masucci, Michael D., Strauss, Gregory P., Kirkpatrick, Brian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7519104/
https://www.ncbi.nlm.nih.gov/pubmed/32978400
http://dx.doi.org/10.1038/s41537-020-00115-2
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author Cohen, Alex S.
Cox, Christopher R.
Le, Thanh P.
Cowan, Tovah
Masucci, Michael D.
Strauss, Gregory P.
Kirkpatrick, Brian
author_facet Cohen, Alex S.
Cox, Christopher R.
Le, Thanh P.
Cowan, Tovah
Masucci, Michael D.
Strauss, Gregory P.
Kirkpatrick, Brian
author_sort Cohen, Alex S.
collection PubMed
description Negative symptoms are a transdiagnostic feature of serious mental illness (SMI) that can be potentially “digitally phenotyped” using objective vocal analysis. In prior studies, vocal measures show low convergence with clinical ratings, potentially because analysis has used small, constrained acoustic feature sets. 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. Accuracy was high (>90%) and was improved when computed separately by speaking task. ML scores were associated with poor cognitive performance and social functioning and were higher in patients with schizophrenia versus depression or mania diagnoses. However, the features identified as most predictive of BvA/Alogia were generally not considered critical to their operational definitions. Implications for validating and implementing digital phenotyping to reduce SMI burden are discussed.
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spelling pubmed-75191042020-10-14 Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia Cohen, Alex S. Cox, Christopher R. Le, Thanh P. Cowan, Tovah Masucci, Michael D. Strauss, Gregory P. Kirkpatrick, Brian NPJ Schizophr Article Negative symptoms are a transdiagnostic feature of serious mental illness (SMI) that can be potentially “digitally phenotyped” using objective vocal analysis. In prior studies, vocal measures show low convergence with clinical ratings, potentially because analysis has used small, constrained acoustic feature sets. 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. Accuracy was high (>90%) and was improved when computed separately by speaking task. ML scores were associated with poor cognitive performance and social functioning and were higher in patients with schizophrenia versus depression or mania diagnoses. However, the features identified as most predictive of BvA/Alogia were generally not considered critical to their operational definitions. Implications for validating and implementing digital phenotyping to reduce SMI burden are discussed. Nature Publishing Group UK 2020-09-25 /pmc/articles/PMC7519104/ /pubmed/32978400 http://dx.doi.org/10.1038/s41537-020-00115-2 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Cohen, Alex S.
Cox, Christopher R.
Le, Thanh P.
Cowan, Tovah
Masucci, Michael D.
Strauss, Gregory P.
Kirkpatrick, Brian
Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia
title Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia
title_full Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia
title_fullStr Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia
title_full_unstemmed Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia
title_short Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia
title_sort using machine learning of computerized vocal expression to measure blunted vocal affect and alogia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7519104/
https://www.ncbi.nlm.nih.gov/pubmed/32978400
http://dx.doi.org/10.1038/s41537-020-00115-2
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