logo
ResearchBunny Logo
Abstract
This study investigated whether machine learning (ML) could accurately model clinically rated blunted vocal affect (BvA) and alogia using a large acoustic feature set from two speaking tasks. High accuracy (>90%) was achieved, improving when tasks were analyzed separately. ML scores correlated with poor cognitive performance and social functioning, and were higher in schizophrenia patients. However, the most predictive vocal features were not those considered central to BvA/alogia definitions. Implications for digital phenotyping in serious mental illness are discussed.
Publisher
npj Schizophrenia
Published On
Sep 25, 2020
Authors
Alex S. Cohen, Christopher R. Cox, Thanh P. Le, Tovah Cowan, Michael D. Masucci, Gregory P. Strauss, Brian Kirkpatrick
Tags
machine learning
blunted vocal affect
alogia
schizophrenia
digital phenotyping
acoustic features

Related Publications

Explore these studies to deepen your understanding of the subject.

Using the interest theory of rights and Hohfeldian taxonomy to address a gap in machine learning methods for legal document analysis
Computer Science

Using the interest theory of rights and Hohfeldian taxonomy to address a gap in machine learning methods for legal document analysis

A. Izzidien

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review
Medicine and Health

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

J. Zhang, F. Zhong, et al.

Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices
Medicine and Health

Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices

V. Gupta, S. Kariotis, et al.

Clustering micropollutants and estimating rate constants of sorption and biodegradation using machine learning approaches
Environmental Studies and Forestry

Clustering micropollutants and estimating rate constants of sorption and biodegradation using machine learning approaches

S. J. Lim, J. Seo, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs—just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny