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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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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. |
format | Online Article Text |
id | pubmed-9349220 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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