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Predictive Big Data Analytics using the UK Biobank Data

The UK Biobank is a rich national health resource that provides enormous opportunities for international researchers to examine, model, and analyze census-like multisource healthcare data. The archive presents several challenges related to aggregation and harmonization of complex data elements, feat...

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Autores principales: Zhou, Yiwang, Zhao, Lu, Zhou, Nina, Zhao, Yi, Marino, Simeone, Wang, Tuo, Sun, Hanbo, Toga, Arthur W, Dinov, Ivo D
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6461626/
https://www.ncbi.nlm.nih.gov/pubmed/30979917
http://dx.doi.org/10.1038/s41598-019-41634-y
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author Zhou, Yiwang
Zhao, Lu
Zhou, Nina
Zhao, Yi
Marino, Simeone
Wang, Tuo
Sun, Hanbo
Toga, Arthur W
Dinov, Ivo D
author_facet Zhou, Yiwang
Zhao, Lu
Zhou, Nina
Zhao, Yi
Marino, Simeone
Wang, Tuo
Sun, Hanbo
Toga, Arthur W
Dinov, Ivo D
author_sort Zhou, Yiwang
collection PubMed
description The UK Biobank is a rich national health resource that provides enormous opportunities for international researchers to examine, model, and analyze census-like multisource healthcare data. The archive presents several challenges related to aggregation and harmonization of complex data elements, feature heterogeneity and salience, and health analytics. Using 7,614 imaging, clinical, and phenotypic features of 9,914 subjects we performed deep computed phenotyping using unsupervised clustering and derived two distinct sub-cohorts. Using parametric and nonparametric tests, we determined the top 20 most salient features contributing to the cluster separation. Our approach generated decision rules to predict the presence and progression of depression or other mental illnesses by jointly representing and modeling the significant clinical and demographic variables along with the derived salient neuroimaging features. We reported consistency and reliability measures of the derived computed phenotypes and the top salient imaging biomarkers that contributed to the unsupervised clustering. This clinical decision support system identified and utilized holistically the most critical biomarkers for predicting mental health, e.g., depression. External validation of this technique on different populations may lead to reducing healthcare expenses and improving the processes of diagnosis, forecasting, and tracking of normal and pathological aging.
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spelling pubmed-64616262019-04-17 Predictive Big Data Analytics using the UK Biobank Data Zhou, Yiwang Zhao, Lu Zhou, Nina Zhao, Yi Marino, Simeone Wang, Tuo Sun, Hanbo Toga, Arthur W Dinov, Ivo D Sci Rep Article The UK Biobank is a rich national health resource that provides enormous opportunities for international researchers to examine, model, and analyze census-like multisource healthcare data. The archive presents several challenges related to aggregation and harmonization of complex data elements, feature heterogeneity and salience, and health analytics. Using 7,614 imaging, clinical, and phenotypic features of 9,914 subjects we performed deep computed phenotyping using unsupervised clustering and derived two distinct sub-cohorts. Using parametric and nonparametric tests, we determined the top 20 most salient features contributing to the cluster separation. Our approach generated decision rules to predict the presence and progression of depression or other mental illnesses by jointly representing and modeling the significant clinical and demographic variables along with the derived salient neuroimaging features. We reported consistency and reliability measures of the derived computed phenotypes and the top salient imaging biomarkers that contributed to the unsupervised clustering. This clinical decision support system identified and utilized holistically the most critical biomarkers for predicting mental health, e.g., depression. External validation of this technique on different populations may lead to reducing healthcare expenses and improving the processes of diagnosis, forecasting, and tracking of normal and pathological aging. Nature Publishing Group UK 2019-04-12 /pmc/articles/PMC6461626/ /pubmed/30979917 http://dx.doi.org/10.1038/s41598-019-41634-y Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Zhou, Yiwang
Zhao, Lu
Zhou, Nina
Zhao, Yi
Marino, Simeone
Wang, Tuo
Sun, Hanbo
Toga, Arthur W
Dinov, Ivo D
Predictive Big Data Analytics using the UK Biobank Data
title Predictive Big Data Analytics using the UK Biobank Data
title_full Predictive Big Data Analytics using the UK Biobank Data
title_fullStr Predictive Big Data Analytics using the UK Biobank Data
title_full_unstemmed Predictive Big Data Analytics using the UK Biobank Data
title_short Predictive Big Data Analytics using the UK Biobank Data
title_sort predictive big data analytics using the uk biobank data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6461626/
https://www.ncbi.nlm.nih.gov/pubmed/30979917
http://dx.doi.org/10.1038/s41598-019-41634-y
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