Applying speech technologies to assess verbal memory in patients with serious mental illness

Abstract Verbal memory deficits are some of the most profound neurocognitive deficits associated with schizophrenia and serious mental illness in general. As yet, their measurement in clinical settings is limited to traditional tests that allow for limited administrations and require substantial res...

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Autores principales:	Terje B. Holmlund, Chelsea Chandler, Peter W. Foltz, Alex S. Cohen, Jian Cheng, Jared C. Bernstein, Elizabeth P. Rosenfeld, Brita Elvevåg
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/d49ca3cce03e4ac5ad4a2115dd822571
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spelling	oai:doaj.org-article:d49ca3cce03e4ac5ad4a2115dd8225712021-12-02T13:34:33ZApplying speech technologies to assess verbal memory in patients with serious mental illness10.1038/s41746-020-0241-72398-6352https://doaj.org/article/d49ca3cce03e4ac5ad4a2115dd8225712020-03-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0241-7https://doaj.org/toc/2398-6352Abstract Verbal memory deficits are some of the most profound neurocognitive deficits associated with schizophrenia and serious mental illness in general. As yet, their measurement in clinical settings is limited to traditional tests that allow for limited administrations and require substantial resources to deploy and score. Therefore, we developed a digital ambulatory verbal memory test with automated scoring, and repeated self-administration via smart devices. One hundred and four adults participated, comprising 25 patients with serious mental illness and 79 healthy volunteers. The study design was successful with high quality speech recordings produced to 92% of prompts (Patients: 86%, Healthy: 96%). The story recalls were both transcribed and scored by humans, and scores generated using natural language processing on transcriptions were comparable to human ratings (R = 0.83, within the range of human-to-human correlations of R = 0.73–0.89). A fully automated approach that scored transcripts generated by automatic speech recognition produced comparable and accurate scores (R = 0.82), with very high correlation to scores derived from human transcripts (R = 0.99). This study demonstrates the viability of leveraging speech technologies to facilitate the frequent assessment of verbal memory for clinical monitoring purposes in psychiatry.Terje B. HolmlundChelsea ChandlerPeter W. FoltzAlex S. CohenJian ChengJared C. BernsteinElizabeth P. RosenfeldBrita ElvevågNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Terje B. Holmlund Chelsea Chandler Peter W. Foltz Alex S. Cohen Jian Cheng Jared C. Bernstein Elizabeth P. Rosenfeld Brita Elvevåg Applying speech technologies to assess verbal memory in patients with serious mental illness
description	Abstract Verbal memory deficits are some of the most profound neurocognitive deficits associated with schizophrenia and serious mental illness in general. As yet, their measurement in clinical settings is limited to traditional tests that allow for limited administrations and require substantial resources to deploy and score. Therefore, we developed a digital ambulatory verbal memory test with automated scoring, and repeated self-administration via smart devices. One hundred and four adults participated, comprising 25 patients with serious mental illness and 79 healthy volunteers. The study design was successful with high quality speech recordings produced to 92% of prompts (Patients: 86%, Healthy: 96%). The story recalls were both transcribed and scored by humans, and scores generated using natural language processing on transcriptions were comparable to human ratings (R = 0.83, within the range of human-to-human correlations of R = 0.73–0.89). A fully automated approach that scored transcripts generated by automatic speech recognition produced comparable and accurate scores (R = 0.82), with very high correlation to scores derived from human transcripts (R = 0.99). This study demonstrates the viability of leveraging speech technologies to facilitate the frequent assessment of verbal memory for clinical monitoring purposes in psychiatry.
format	article
author	Terje B. Holmlund Chelsea Chandler Peter W. Foltz Alex S. Cohen Jian Cheng Jared C. Bernstein Elizabeth P. Rosenfeld Brita Elvevåg
author_facet	Terje B. Holmlund Chelsea Chandler Peter W. Foltz Alex S. Cohen Jian Cheng Jared C. Bernstein Elizabeth P. Rosenfeld Brita Elvevåg
author_sort	Terje B. Holmlund
title	Applying speech technologies to assess verbal memory in patients with serious mental illness
title_short	Applying speech technologies to assess verbal memory in patients with serious mental illness
title_full	Applying speech technologies to assess verbal memory in patients with serious mental illness
title_fullStr	Applying speech technologies to assess verbal memory in patients with serious mental illness
title_full_unstemmed	Applying speech technologies to assess verbal memory in patients with serious mental illness
title_sort	applying speech technologies to assess verbal memory in patients with serious mental illness
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/d49ca3cce03e4ac5ad4a2115dd822571
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Applying speech technologies to assess verbal memory in patients with serious mental illness

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