A Configurable Deep Network for high-dimensional clinical trial data

Abstract

Clinical studies provide interesting case studies for data mining researchers, given the often high degree of dimensionality and long term nature of these studies. In areas such as dementia, accurate predictions from data scientists provide vital input into the understanding of how certain features (representing lifestyle) can predict outcomes such as dementia. Most research involved has used traditional or shallow data mining approaches which have been shown to offer varying degrees of accuracy in datasets with high dimensionality. In this research, we explore the use of deep learning architectures, as they have been shown to have high predictive capabilities in image and audio datasets. The purpose of our research is to build a framework which allows easy reconfiguration for the performance of experiments across a number of deep learning approaches. In this paper, we present our framework for a configurable deep learning machine and our evaluation and analysis of two shallow approaches: regression and multi-layer perceptron, as a platform to a deep belief network, and using a dataset created over the course of 12 years by researchers in the area of dementia.

DOI: 10.1109/IJCNN.2015.7280841

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Cite this paper

@article{ODonoghue2015ACD, title={A Configurable Deep Network for high-dimensional clinical trial data}, author={Jim O'Donoghue and Mark Roantree and Martin van Boxtel}, journal={2015 International Joint Conference on Neural Networks (IJCNN)}, year={2015}, pages={1-8} }