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Identifying neurocognitive disorder using vector representation of free conversation

In recent years, studies on the use of natural language processing (NLP) approaches to identify dementia have been reported. Most of these studies used picture description tasks or other similar tasks to encourage spontaneous speech, but the use of free conversation without requiring a task might be...

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Autores principales: Horigome, Toshiro, Hino, Kimihiro, Toyoshiba, Hiroyoshi, Shindo, Norihisa, Funaki, Kei, Eguchi, Yoko, Kitazawa, Momoko, Fujita, Takanori, Mimura, Masaru, Kishimoto, Taishiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9349220/
https://www.ncbi.nlm.nih.gov/pubmed/35922457
http://dx.doi.org/10.1038/s41598-022-16204-4
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author Horigome, Toshiro
Hino, Kimihiro
Toyoshiba, Hiroyoshi
Shindo, Norihisa
Funaki, Kei
Eguchi, Yoko
Kitazawa, Momoko
Fujita, Takanori
Mimura, Masaru
Kishimoto, Taishiro
author_facet Horigome, Toshiro
Hino, Kimihiro
Toyoshiba, Hiroyoshi
Shindo, Norihisa
Funaki, Kei
Eguchi, Yoko
Kitazawa, Momoko
Fujita, Takanori
Mimura, Masaru
Kishimoto, Taishiro
author_sort Horigome, Toshiro
collection PubMed
description In recent years, studies on the use of natural language processing (NLP) approaches to identify dementia have been reported. Most of these studies used picture description tasks or other similar tasks to encourage spontaneous speech, but the use of free conversation without requiring a task might be easier to perform in a clinical setting. Moreover, free conversation is unlikely to induce a learning effect. Therefore, the purpose of this study was to develop a machine learning model to discriminate subjects with and without dementia by extracting features from unstructured free conversation data using NLP. We recruited patients who visited a specialized outpatient clinic for dementia and healthy volunteers. Participants’ conversation was transcribed and the text data was decomposed from natural sentences into morphemes by performing a morphological analysis using NLP, and then converted into real-valued vectors that were used as features for machine learning. A total of 432 datasets were used, and the resulting machine learning model classified the data for dementia and non-dementia subjects with an accuracy of 0.900, sensitivity of 0.881, and a specificity of 0.916. Using sentence vector information, it was possible to develop a machine-learning algorithm capable of discriminating dementia from non-dementia subjects with a high accuracy based on free conversation.
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spelling pubmed-93492202022-08-05 Identifying neurocognitive disorder using vector representation of free conversation Horigome, Toshiro Hino, Kimihiro Toyoshiba, Hiroyoshi Shindo, Norihisa Funaki, Kei Eguchi, Yoko Kitazawa, Momoko Fujita, Takanori Mimura, Masaru Kishimoto, Taishiro Sci Rep Article In recent years, studies on the use of natural language processing (NLP) approaches to identify dementia have been reported. Most of these studies used picture description tasks or other similar tasks to encourage spontaneous speech, but the use of free conversation without requiring a task might be easier to perform in a clinical setting. Moreover, free conversation is unlikely to induce a learning effect. Therefore, the purpose of this study was to develop a machine learning model to discriminate subjects with and without dementia by extracting features from unstructured free conversation data using NLP. We recruited patients who visited a specialized outpatient clinic for dementia and healthy volunteers. Participants’ conversation was transcribed and the text data was decomposed from natural sentences into morphemes by performing a morphological analysis using NLP, and then converted into real-valued vectors that were used as features for machine learning. A total of 432 datasets were used, and the resulting machine learning model classified the data for dementia and non-dementia subjects with an accuracy of 0.900, sensitivity of 0.881, and a specificity of 0.916. Using sentence vector information, it was possible to develop a machine-learning algorithm capable of discriminating dementia from non-dementia subjects with a high accuracy based on free conversation. Nature Publishing Group UK 2022-08-03 /pmc/articles/PMC9349220/ /pubmed/35922457 http://dx.doi.org/10.1038/s41598-022-16204-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Horigome, Toshiro
Hino, Kimihiro
Toyoshiba, Hiroyoshi
Shindo, Norihisa
Funaki, Kei
Eguchi, Yoko
Kitazawa, Momoko
Fujita, Takanori
Mimura, Masaru
Kishimoto, Taishiro
Identifying neurocognitive disorder using vector representation of free conversation
title Identifying neurocognitive disorder using vector representation of free conversation
title_full Identifying neurocognitive disorder using vector representation of free conversation
title_fullStr Identifying neurocognitive disorder using vector representation of free conversation
title_full_unstemmed Identifying neurocognitive disorder using vector representation of free conversation
title_short Identifying neurocognitive disorder using vector representation of free conversation
title_sort identifying neurocognitive disorder using vector representation of free conversation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9349220/
https://www.ncbi.nlm.nih.gov/pubmed/35922457
http://dx.doi.org/10.1038/s41598-022-16204-4
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