| Title: | Predictive Big Data Analytics using the UK Biobank Data |
| Journal: | Scientific Reports |
| Published: | 12 Apr 2019 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/30979917/ |
| DOI: | https://doi.org/10.1038/s41598-019-41634-y |
| URL: | https://www.nature.com/articles/s41598-019-41634-y.pdf |
Publication 7328
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Abstract
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.9 Authors
- Yiwang Zhou
- Lu Zhao
- Nina Zhou
- Yi Zhao
- Simeone Marino
- Tuo Wang
- Hanbo Sun
- Arthur W Toga
- Ivo D Dinov
Enabling scientific discoveries that improve human health