An efficient context-aware screening system for Alzheimer's disease based on neuropsychology test

Abstract Alzheimer's disease (AD) and other dementias have become the fifth leading cause of death worldwide. Accurate early detection of the disease and its precursor, Mild Cognitive Impairment (MCI), is crucial to alleviate the burden on the healthcare system. While most of the existing work...

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Autores principales:	Austin Cheng-Yun Tsai, Sheng-Yi Hong, Li-Hung Yao, Wei-Der Chang, Li-Chen Fu, Yu-Ling Chang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7b8c8212b95f44749ada4663eeea7cfb
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spelling	oai:doaj.org-article:7b8c8212b95f44749ada4663eeea7cfb2021-12-02T18:02:22ZAn efficient context-aware screening system for Alzheimer's disease based on neuropsychology test10.1038/s41598-021-97642-42045-2322https://doaj.org/article/7b8c8212b95f44749ada4663eeea7cfb2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97642-4https://doaj.org/toc/2045-2322Abstract Alzheimer's disease (AD) and other dementias have become the fifth leading cause of death worldwide. Accurate early detection of the disease and its precursor, Mild Cognitive Impairment (MCI), is crucial to alleviate the burden on the healthcare system. While most of the existing work in the literature applied neural networks directly together with several data pre-processing techniques, we proposed in this paper a screening system that is to perform classification based on automatic processing of the transcripts of speeches from the subjects undertaking a neuropsychological test. Our system is also shown applicable to different datasets and languages, suggesting that our system holds a high potential to be deployed widely in hospitals across regions. We conducted comprehensive experiments on two different languages datasets, the Pitt dataset and the NTUHV dataset, to validate our study. The results showed that our proposed system significantly outperformed the previous works on both datasets, with the score of the area under the receiver operating characteristic curve (AUROC) of classifying AD and healthy control (HC) being as high as 0.92 on the Pitt dataset and 0.97 on the NTUHV dataset. The performance on classifying MCI and HC remained promising, with the AUROC being 0.83 on the Pitt dataset and 0.88 on the NTUHV dataset.Austin Cheng-Yun TsaiSheng-Yi HongLi-Hung YaoWei-Der ChangLi-Chen FuYu-Ling ChangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Austin Cheng-Yun Tsai Sheng-Yi Hong Li-Hung Yao Wei-Der Chang Li-Chen Fu Yu-Ling Chang An efficient context-aware screening system for Alzheimer's disease based on neuropsychology test
description	Abstract Alzheimer's disease (AD) and other dementias have become the fifth leading cause of death worldwide. Accurate early detection of the disease and its precursor, Mild Cognitive Impairment (MCI), is crucial to alleviate the burden on the healthcare system. While most of the existing work in the literature applied neural networks directly together with several data pre-processing techniques, we proposed in this paper a screening system that is to perform classification based on automatic processing of the transcripts of speeches from the subjects undertaking a neuropsychological test. Our system is also shown applicable to different datasets and languages, suggesting that our system holds a high potential to be deployed widely in hospitals across regions. We conducted comprehensive experiments on two different languages datasets, the Pitt dataset and the NTUHV dataset, to validate our study. The results showed that our proposed system significantly outperformed the previous works on both datasets, with the score of the area under the receiver operating characteristic curve (AUROC) of classifying AD and healthy control (HC) being as high as 0.92 on the Pitt dataset and 0.97 on the NTUHV dataset. The performance on classifying MCI and HC remained promising, with the AUROC being 0.83 on the Pitt dataset and 0.88 on the NTUHV dataset.
format	article
author	Austin Cheng-Yun Tsai Sheng-Yi Hong Li-Hung Yao Wei-Der Chang Li-Chen Fu Yu-Ling Chang
author_facet	Austin Cheng-Yun Tsai Sheng-Yi Hong Li-Hung Yao Wei-Der Chang Li-Chen Fu Yu-Ling Chang
author_sort	Austin Cheng-Yun Tsai
title	An efficient context-aware screening system for Alzheimer's disease based on neuropsychology test
title_short	An efficient context-aware screening system for Alzheimer's disease based on neuropsychology test
title_full	An efficient context-aware screening system for Alzheimer's disease based on neuropsychology test
title_fullStr	An efficient context-aware screening system for Alzheimer's disease based on neuropsychology test
title_full_unstemmed	An efficient context-aware screening system for Alzheimer's disease based on neuropsychology test
title_sort	efficient context-aware screening system for alzheimer's disease based on neuropsychology test
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7b8c8212b95f44749ada4663eeea7cfb
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An efficient context-aware screening system for Alzheimer's disease based on neuropsychology test

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