scholarly journals A novel framework for bringing smart big data to proactive decision making in healthcare

2021 ◽  
Vol 27 (2) ◽  
pp. 146045822110246
Author(s):  
Shengyao Zhou ◽  
Runtong Zhang ◽  
Donghua Chen ◽  
Xiaomin Zhu

Big data have shown their great potential value to serve many aspects of human life. Due to complexity of the medical and healthcare big data in real life, traditional big data analysis methods are difficult to be dealt with. Therefore, a single method is unable to analyze and manage heterogeneous big data sources. To utilize data fully from the perspective of decision-making, we propose a novel framework which guides the healthcare big data to be smartly and proactively processed for decision-making without user interventions. The framework contains five stages, which are intelligent data cleaning, customized data fusion, analysis mapping, exploratory visualization analysis, and generation of decision-making reports. It also enables learning from the data and correlating them with the existing human knowledge. Subsequently, a smart big data-driven application exhibits innovative management in intelligent healthcare. The proposed framework provides the guidelines of the best practices of big data-driven analysis for intelligent healthcare according to our practical applications. The platform provides the appropriate reference for the big data-driven innovation of management in intelligent healthcare.

Author(s):  
Sreenu G. ◽  
M.A. Saleem Durai

Advances in recent hardware technology have permitted to document transactions and other pieces of information of everyday life at an express pace. In addition of speed up and storage capacity, real-life perceptions tend to transform over time. However, there are so much prospective and highly functional values unseen in the vast volume of data. For this kind of applications conventional data mining is not suitable, so they should be tuned and changed or designed with new algorithms. Big data computing is inflowing to the category of most hopeful technologies that shows the way to new ways of thinking and decision making. This epoch of big data helps users to take benefit out of all available data to gain more precise systematic results or determine latent information, and then make best possible decisions. Depiction from a broad set of workloads, the author establishes a set of classifying measures based on the storage architecture, processing types, processing techniques and the tools and technologies used.


Author(s):  
Cheng Meng ◽  
Ye Wang ◽  
Xinlian Zhang ◽  
Abhyuday Mandal ◽  
Wenxuan Zhong ◽  
...  

With advances in technologies in the past decade, the amount of data generated and recorded has grown enormously in virtually all fields of industry and science. This extraordinary amount of data provides unprecedented opportunities for data-driven decision-making and knowledge discovery. However, the task of analyzing such large-scale dataset poses significant challenges and calls for innovative statistical methods specifically designed for faster speed and higher efficiency. In this chapter, we review currently available methods for big data, with a focus on the subsampling methods using statistical leveraging and divide and conquer methods.


Procedia CIRP ◽  
2019 ◽  
Vol 83 ◽  
pp. 814-818 ◽  
Author(s):  
Yongheng Zhang ◽  
Rui Zhang ◽  
Yizhong Wang ◽  
Hongfei Guo ◽  
Ray Y Zhong ◽  
...  

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