Title

Enhancing health risk prediction with deep learning on big data and revised fusion node paradigm

Document Type

Journal Article

Publisher

Hindawi

Place of Publication

United States

School

School of Science

Comments

Originally published as: Zhong, H., & Xiao, J. (2017). Enhancing health risk prediction with deep learning on big data and revised fusion node paradigm. Scientific Programming, Article ID 1901876, 1-19. Original article available here.

Abstract

With recent advances in health systems, the amount of health data is expanding rapidly in various formats. This data originates from many new sources including digital records, mobile devices, and wearable health devices. Big health data offers more opportunities for health data analysis and enhancement of health services via innovative approaches. The objective of this research is to develop a framework to enhance health prediction with the revised fusion node and deep learning paradigms. Fusion node is an information fusion model for constructing prediction systems. Deep learning involves the complex application of machine-learning algorithms, such as Bayesian fusions and neural network, for data extraction and logical inference. Deep learning, combined with information fusion paradigms, can be utilized to provide more comprehensive and reliable predictions from big health data. Based on the proposed framework, an experimental system is developed as an illustration for the framework implementation

DOI

10.1155/2017/1901876

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