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Title: Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations

Abstract

Background A unique archive of Big Data on Parkinson’s Disease is collected, managed and disseminated by the Parkinson’s Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson’s disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data–large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources–all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Methods and Findings Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing,more » cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson’s disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. Conclusions Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson’s disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer’s, Huntington’s, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.« less

Authors:
 [1];  [2];  [3];  [2];  [4];  [5];  [4];  [6];  [7];  [5];  [4];  [2];  [2];  [6];  [6];  [6];  [2];  [8];  [7];  [6] more »; « less
+ Show Author Affiliations
  1. Univ. of Michigan, Ann Arbor, MI (United States). Statistics Online Computational Resource, School of Nursing, Michigan Institute for Data Science; Univ. of Southern California, Los Angeles, CA (United States). Stevens Neuroimaging and Informatics Institute; ; Univ. of Michigan, Ann Arbor, MI (United States). Udall Center of Excellence for Parkinson’s Disease Research
  2. Institute for Systems Biology, Seattle, Washington, (United States)
  3. Univ. of Michigan, Ann Arbor, MI (United States). Statistics Online Computational Resource, School of Nursing, Michigan Institute for Data Science
  4. Argonne National Lab. (ANL), Argonne, IL (United States); ; Univ. of Chicago, IL (United States). Computation Institute
  5. Univ. of Southern California, Los Angeles, CA (United States). Information Sciences Institute
  6. Univ. of Southern California, Los Angeles, CA (United States). Stevens Neuroimaging and Informatics Institute
  7. Univ. of Michigan, Ann Arbor, MI (United States). Udall Center of Excellence for Parkinson’s Disease Research
  8. Univ. of Michigan, Ann Arbor, MI (United States). Department of Psychiatry and Michigan Alzheimer’s Disease Center; Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan, (United States)
Publication Date:
Sponsoring Org.:
USDOE; National Science Foundation (NSF); National Institutes of Health (NIH)
OSTI Identifier:
1627792
Grant/Contract Number:  
AC02-06CH11357; 1023115; 1022560; 1022636; 0089377; 9652870; 0442992; 0442630; 0333672; 0716055; P20 NR015331; P50 NS091856; P30 DK089503; U54 EB02040
Resource Type:
Accepted Manuscript
Journal Name:
PLoS ONE
Additional Journal Information:
Journal Volume: 11; Journal Issue: 8; Journal ID: ISSN 1932-6203
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
Science & Technology - Other Topics

Citation Formats

Dinov, Ivo D., Heavner, Ben, Tang, Ming, Glusman, Gustavo, Chard, Kyle, Darcy, Mike, Madduri, Ravi, Pa, Judy, Spino, Cathie, Kesselman, Carl, Foster, Ian, Deutsch, Eric W., Price, Nathan D., Van Horn, John D., Ames, Joseph, Clark, Kristi, Hood, Leroy, Hampstead, Benjamin M., Dauer, William, Toga, Arthur W., and Draganski, Bogdan. Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations. United States: N. p., 2016. Web. doi:10.1371/journal.pone.0157077.
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Dinov, Ivo D., Heavner, Ben, Tang, Ming, Glusman, Gustavo, Chard, Kyle, Darcy, Mike, Madduri, Ravi, Pa, Judy, Spino, Cathie, Kesselman, Carl, Foster, Ian, Deutsch, Eric W., Price, Nathan D., Van Horn, John D., Ames, Joseph, Clark, Kristi, Hood, Leroy, Hampstead, Benjamin M., Dauer, William, Toga, Arthur W., & Draganski, Bogdan. Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations. United States. https://doi.org/10.1371/journal.pone.0157077
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Dinov, Ivo D., Heavner, Ben, Tang, Ming, Glusman, Gustavo, Chard, Kyle, Darcy, Mike, Madduri, Ravi, Pa, Judy, Spino, Cathie, Kesselman, Carl, Foster, Ian, Deutsch, Eric W., Price, Nathan D., Van Horn, John D., Ames, Joseph, Clark, Kristi, Hood, Leroy, Hampstead, Benjamin M., Dauer, William, Toga, Arthur W., and Draganski, Bogdan. Fri . "Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations". United States. https://doi.org/10.1371/journal.pone.0157077. https://www.osti.gov/servlets/purl/1627792.
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@article{osti_1627792,
title = {Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations},
author = {Dinov, Ivo D. and Heavner, Ben and Tang, Ming and Glusman, Gustavo and Chard, Kyle and Darcy, Mike and Madduri, Ravi and Pa, Judy and Spino, Cathie and Kesselman, Carl and Foster, Ian and Deutsch, Eric W. and Price, Nathan D. and Van Horn, John D. and Ames, Joseph and Clark, Kristi and Hood, Leroy and Hampstead, Benjamin M. and Dauer, William and Toga, Arthur W. and Draganski, Bogdan},
abstractNote = {Background A unique archive of Big Data on Parkinson’s Disease is collected, managed and disseminated by the Parkinson’s Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson’s disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data–large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources–all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Methods and Findings Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson’s disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. Conclusions Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson’s disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer’s, Huntington’s, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.},
doi = {10.1371/journal.pone.0157077},
journal = {PLoS ONE},
number = 8,
volume = 11,
place = {United States},
year = {Fri Aug 05 00:00:00 EDT 2016},
month = {Fri Aug 05 00:00:00 EDT 2016}
}
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Fig 1 Fig 1: Exponential growth model of PD prevalence with age.
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    journal, May 2018

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    journal, September 2017

    • Borgermans, Liesbeth; Devroey, Dirk
    • International Journal of Integrated Care, Vol. 17, Issue 4
    • DOI: 10.5334/ijic.3295
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