Hierarchical interactions model for predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) conversion.

Identifying patients with Mild Cognitive Impairment (MCI) who are likely to convert to dementia has recently attracted increasing attention in Alzheimer's disease (AD) research. An accurate prediction of conversion from MCI to AD can aid clinicians to initiate treatments at early stage and moni...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Han Li, Yashu Liu, Pinghua Gong, Changshui Zhang, Jieping Ye, Alzheimers Disease Neuroimaging Initiative
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5bb621d26c18474f8aac196bcfdfbff1
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5bb621d26c18474f8aac196bcfdfbff1
record_format	dspace
spelling	oai:doaj.org-article:5bb621d26c18474f8aac196bcfdfbff12021-11-18T08:38:41ZHierarchical interactions model for predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) conversion.1932-620310.1371/journal.pone.0082450https://doaj.org/article/5bb621d26c18474f8aac196bcfdfbff12014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24416143/?tool=EBIhttps://doaj.org/toc/1932-6203Identifying patients with Mild Cognitive Impairment (MCI) who are likely to convert to dementia has recently attracted increasing attention in Alzheimer's disease (AD) research. An accurate prediction of conversion from MCI to AD can aid clinicians to initiate treatments at early stage and monitor their effectiveness. However, existing prediction systems based on the original biosignatures are not satisfactory. In this paper, we propose to fit the prediction models using pairwise biosignature interactions, thus capturing higher-order relationship among biosignatures. Specifically, we employ hierarchical constraints and sparsity regularization to prune the high-dimensional input features. Based on the significant biosignatures and underlying interactions identified, we build classifiers to predict the conversion probability based on the selected features. We further analyze the underlying interaction effects of different biosignatures based on the so-called stable expectation scores. We have used 293 MCI subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) database that have MRI measurements at the baseline to evaluate the effectiveness of the proposed method. Our proposed method achieves better classification performance than state-of-the-art methods. Moreover, we discover several significant interactions predictive of MCI-to-AD conversion. These results shed light on improving the prediction performance using interaction features.Han LiYashu LiuPinghua GongChangshui ZhangJieping YeAlzheimers Disease Neuroimaging InitiativePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 1, p e82450 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Han Li Yashu Liu Pinghua Gong Changshui Zhang Jieping Ye Alzheimers Disease Neuroimaging Initiative Hierarchical interactions model for predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) conversion.
description	Identifying patients with Mild Cognitive Impairment (MCI) who are likely to convert to dementia has recently attracted increasing attention in Alzheimer's disease (AD) research. An accurate prediction of conversion from MCI to AD can aid clinicians to initiate treatments at early stage and monitor their effectiveness. However, existing prediction systems based on the original biosignatures are not satisfactory. In this paper, we propose to fit the prediction models using pairwise biosignature interactions, thus capturing higher-order relationship among biosignatures. Specifically, we employ hierarchical constraints and sparsity regularization to prune the high-dimensional input features. Based on the significant biosignatures and underlying interactions identified, we build classifiers to predict the conversion probability based on the selected features. We further analyze the underlying interaction effects of different biosignatures based on the so-called stable expectation scores. We have used 293 MCI subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) database that have MRI measurements at the baseline to evaluate the effectiveness of the proposed method. Our proposed method achieves better classification performance than state-of-the-art methods. Moreover, we discover several significant interactions predictive of MCI-to-AD conversion. These results shed light on improving the prediction performance using interaction features.
format	article
author	Han Li Yashu Liu Pinghua Gong Changshui Zhang Jieping Ye Alzheimers Disease Neuroimaging Initiative
author_facet	Han Li Yashu Liu Pinghua Gong Changshui Zhang Jieping Ye Alzheimers Disease Neuroimaging Initiative
author_sort	Han Li
title	Hierarchical interactions model for predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) conversion.
title_short	Hierarchical interactions model for predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) conversion.
title_full	Hierarchical interactions model for predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) conversion.
title_fullStr	Hierarchical interactions model for predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) conversion.
title_full_unstemmed	Hierarchical interactions model for predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) conversion.
title_sort	hierarchical interactions model for predicting mild cognitive impairment (mci) to alzheimer's disease (ad) conversion.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/5bb621d26c18474f8aac196bcfdfbff1
work_keys_str_mv	AT hanli hierarchicalinteractionsmodelforpredictingmildcognitiveimpairmentmcitoalzheimersdiseaseadconversion AT yashuliu hierarchicalinteractionsmodelforpredictingmildcognitiveimpairmentmcitoalzheimersdiseaseadconversion AT pinghuagong hierarchicalinteractionsmodelforpredictingmildcognitiveimpairmentmcitoalzheimersdiseaseadconversion AT changshuizhang hierarchicalinteractionsmodelforpredictingmildcognitiveimpairmentmcitoalzheimersdiseaseadconversion AT jiepingye hierarchicalinteractionsmodelforpredictingmildcognitiveimpairmentmcitoalzheimersdiseaseadconversion AT alzheimersdiseaseneuroimaginginitiative hierarchicalinteractionsmodelforpredictingmildcognitiveimpairmentmcitoalzheimersdiseaseadconversion
_version_	1718421502501584896

Hierarchical interactions model for predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) conversion.

Ejemplares similares