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A Transfer Learning Method for Detecting Alzheimer's Disease Based on Speech and Natural Language Processing
Alzheimer's disease (AD) is a neurodegenerative disease that is difficult to be detected using convenient and reliable methods. The language change in patients with AD is an important signal of their cognitive status, which potentially helps in early diagnosis. In this study, we developed a tra...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9043451/ https://www.ncbi.nlm.nih.gov/pubmed/35493375 http://dx.doi.org/10.3389/fpubh.2022.772592 |
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author | Liu, Ning Luo, Kexue Yuan, Zhenming Chen, Yan |
author_facet | Liu, Ning Luo, Kexue Yuan, Zhenming Chen, Yan |
author_sort | Liu, Ning |
collection | PubMed |
description | Alzheimer's disease (AD) is a neurodegenerative disease that is difficult to be detected using convenient and reliable methods. The language change in patients with AD is an important signal of their cognitive status, which potentially helps in early diagnosis. In this study, we developed a transfer learning model based on speech and natural language processing (NLP) technology for the early diagnosis of AD. The lack of large datasets limits the use of complex neural network models without feature engineering, while transfer learning can effectively solve this problem. The transfer learning model is firstly pre-trained on large text datasets to get the pre-trained language model, and then, based on such a model, an AD classification model is performed on small training sets. Concretely, a distilled bidirectional encoder representation (distilBert) embedding, combined with a logistic regression classifier, is used to distinguish AD from normal controls. The model experiment was evaluated on Alzheimer's dementia recognition through spontaneous speech datasets in 2020, including the balanced 78 healthy controls (HC) and 78 patients with AD. The accuracy of the proposed model is 0.88, which is almost equivalent to the champion score in the challenge and a considerable improvement over the baseline of 75% established by organizers of the challenge. As a result, the transfer learning method in this study improves AD prediction, which does not only reduces the need for feature engineering but also addresses the lack of sufficiently large datasets. |
format | Online Article Text |
id | pubmed-9043451 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90434512022-04-28 A Transfer Learning Method for Detecting Alzheimer's Disease Based on Speech and Natural Language Processing Liu, Ning Luo, Kexue Yuan, Zhenming Chen, Yan Front Public Health Public Health Alzheimer's disease (AD) is a neurodegenerative disease that is difficult to be detected using convenient and reliable methods. The language change in patients with AD is an important signal of their cognitive status, which potentially helps in early diagnosis. In this study, we developed a transfer learning model based on speech and natural language processing (NLP) technology for the early diagnosis of AD. The lack of large datasets limits the use of complex neural network models without feature engineering, while transfer learning can effectively solve this problem. The transfer learning model is firstly pre-trained on large text datasets to get the pre-trained language model, and then, based on such a model, an AD classification model is performed on small training sets. Concretely, a distilled bidirectional encoder representation (distilBert) embedding, combined with a logistic regression classifier, is used to distinguish AD from normal controls. The model experiment was evaluated on Alzheimer's dementia recognition through spontaneous speech datasets in 2020, including the balanced 78 healthy controls (HC) and 78 patients with AD. The accuracy of the proposed model is 0.88, which is almost equivalent to the champion score in the challenge and a considerable improvement over the baseline of 75% established by organizers of the challenge. As a result, the transfer learning method in this study improves AD prediction, which does not only reduces the need for feature engineering but also addresses the lack of sufficiently large datasets. Frontiers Media S.A. 2022-04-13 /pmc/articles/PMC9043451/ /pubmed/35493375 http://dx.doi.org/10.3389/fpubh.2022.772592 Text en Copyright © 2022 Liu, Luo, Yuan and Chen. 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 | Public Health Liu, Ning Luo, Kexue Yuan, Zhenming Chen, Yan A Transfer Learning Method for Detecting Alzheimer's Disease Based on Speech and Natural Language Processing |
title | A Transfer Learning Method for Detecting Alzheimer's Disease Based on Speech and Natural Language Processing |
title_full | A Transfer Learning Method for Detecting Alzheimer's Disease Based on Speech and Natural Language Processing |
title_fullStr | A Transfer Learning Method for Detecting Alzheimer's Disease Based on Speech and Natural Language Processing |
title_full_unstemmed | A Transfer Learning Method for Detecting Alzheimer's Disease Based on Speech and Natural Language Processing |
title_short | A Transfer Learning Method for Detecting Alzheimer's Disease Based on Speech and Natural Language Processing |
title_sort | transfer learning method for detecting alzheimer's disease based on speech and natural language processing |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9043451/ https://www.ncbi.nlm.nih.gov/pubmed/35493375 http://dx.doi.org/10.3389/fpubh.2022.772592 |
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