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Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers

BACKGROUND: The manual diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning a...

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Autores principales: Orimaye, Sylvester O., Wong, Jojo S-M., Golden, Karen J., Wong, Chee P., Soyiri, Ireneous N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5237556/
https://www.ncbi.nlm.nih.gov/pubmed/28088191
http://dx.doi.org/10.1186/s12859-016-1456-0
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author Orimaye, Sylvester O.
Wong, Jojo S-M.
Golden, Karen J.
Wong, Chee P.
Soyiri, Ireneous N.
author_facet Orimaye, Sylvester O.
Wong, Jojo S-M.
Golden, Karen J.
Wong, Chee P.
Soyiri, Ireneous N.
author_sort Orimaye, Sylvester O.
collection PubMed
description BACKGROUND: The manual diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls. RESULTS: Our models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM). CONCLUSIONS: Experimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1456-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-52375562017-01-18 Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers Orimaye, Sylvester O. Wong, Jojo S-M. Golden, Karen J. Wong, Chee P. Soyiri, Ireneous N. BMC Bioinformatics Research Article BACKGROUND: The manual diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls. RESULTS: Our models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM). CONCLUSIONS: Experimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1456-0) contains supplementary material, which is available to authorized users. BioMed Central 2017-01-14 /pmc/articles/PMC5237556/ /pubmed/28088191 http://dx.doi.org/10.1186/s12859-016-1456-0 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Orimaye, Sylvester O.
Wong, Jojo S-M.
Golden, Karen J.
Wong, Chee P.
Soyiri, Ireneous N.
Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers
title Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers
title_full Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers
title_fullStr Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers
title_full_unstemmed Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers
title_short Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers
title_sort predicting probable alzheimer’s disease using linguistic deficits and biomarkers
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5237556/
https://www.ncbi.nlm.nih.gov/pubmed/28088191
http://dx.doi.org/10.1186/s12859-016-1456-0
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