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Assessment of adjunct cognitive functioning through intake interviews integrated with natural language processing models
In this article, we developed an interview framework and natural language processing model for estimating cognitive function, based on an intake interview with psychologists in a hospital setting. The questionnaire consisted of 30 questions in five categories. To evaluate the developed interview ite...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10162011/ https://www.ncbi.nlm.nih.gov/pubmed/37153095 http://dx.doi.org/10.3389/fmed.2023.1145314 |
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author | Igarashi, Toshiharu Umeda-Kameyama, Yumi Kojima, Taro Akishita, Masahiro Nihei, Misato |
author_facet | Igarashi, Toshiharu Umeda-Kameyama, Yumi Kojima, Taro Akishita, Masahiro Nihei, Misato |
author_sort | Igarashi, Toshiharu |
collection | PubMed |
description | In this article, we developed an interview framework and natural language processing model for estimating cognitive function, based on an intake interview with psychologists in a hospital setting. The questionnaire consisted of 30 questions in five categories. To evaluate the developed interview items and the accuracy of the natural language processing model, we recruited participants with the approval of the University of Tokyo Hospital and obtained the cooperation of 29 participants (7 men and 22 women) aged 72–91 years. Based on the MMSE results, a multilevel classification model was created to classify the three groups, and a binary classification model to sort the two groups. For each of these models, we tested whether the accuracy would improve when text augmentation was performed. The accuracy in the multi-level classification results for the test data was 0.405 without augmentation and 0.991 with augmentation. The accuracy of the test data in the results of the binary classification without augmentation was 0.488 for the moderate dementia and mild dementia groups, 0.767 for the moderate dementia and MCI groups, and 0.700 for the mild dementia and MCI groups. In contrast, the accuracy of the test data in the augmented binary classification results was 0.972 for moderate dementia and mild dementia groups, 0.996 for moderate dementia and MCI groups, and 0.985 for mild dementia and MCI groups. |
format | Online Article Text |
id | pubmed-10162011 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101620112023-05-06 Assessment of adjunct cognitive functioning through intake interviews integrated with natural language processing models Igarashi, Toshiharu Umeda-Kameyama, Yumi Kojima, Taro Akishita, Masahiro Nihei, Misato Front Med (Lausanne) Medicine In this article, we developed an interview framework and natural language processing model for estimating cognitive function, based on an intake interview with psychologists in a hospital setting. The questionnaire consisted of 30 questions in five categories. To evaluate the developed interview items and the accuracy of the natural language processing model, we recruited participants with the approval of the University of Tokyo Hospital and obtained the cooperation of 29 participants (7 men and 22 women) aged 72–91 years. Based on the MMSE results, a multilevel classification model was created to classify the three groups, and a binary classification model to sort the two groups. For each of these models, we tested whether the accuracy would improve when text augmentation was performed. The accuracy in the multi-level classification results for the test data was 0.405 without augmentation and 0.991 with augmentation. The accuracy of the test data in the results of the binary classification without augmentation was 0.488 for the moderate dementia and mild dementia groups, 0.767 for the moderate dementia and MCI groups, and 0.700 for the mild dementia and MCI groups. In contrast, the accuracy of the test data in the augmented binary classification results was 0.972 for moderate dementia and mild dementia groups, 0.996 for moderate dementia and MCI groups, and 0.985 for mild dementia and MCI groups. Frontiers Media S.A. 2023-04-21 /pmc/articles/PMC10162011/ /pubmed/37153095 http://dx.doi.org/10.3389/fmed.2023.1145314 Text en Copyright © 2023 Igarashi, Umeda-Kameyama, Kojima, Akishita and Nihei. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Igarashi, Toshiharu Umeda-Kameyama, Yumi Kojima, Taro Akishita, Masahiro Nihei, Misato Assessment of adjunct cognitive functioning through intake interviews integrated with natural language processing models |
title | Assessment of adjunct cognitive functioning through intake interviews integrated with natural language processing models |
title_full | Assessment of adjunct cognitive functioning through intake interviews integrated with natural language processing models |
title_fullStr | Assessment of adjunct cognitive functioning through intake interviews integrated with natural language processing models |
title_full_unstemmed | Assessment of adjunct cognitive functioning through intake interviews integrated with natural language processing models |
title_short | Assessment of adjunct cognitive functioning through intake interviews integrated with natural language processing models |
title_sort | assessment of adjunct cognitive functioning through intake interviews integrated with natural language processing models |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10162011/ https://www.ncbi.nlm.nih.gov/pubmed/37153095 http://dx.doi.org/10.3389/fmed.2023.1145314 |
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