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Sensors 2015, 15(7), 15772-15798; doi:10.3390/s150715772

GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare

1
Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea
2
Department of Computing and Information Systems, University of Tasmania, Hobart Tasmania 7005, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 15 April 2015 / Revised: 23 June 2015 / Accepted: 24 June 2015 / Published: 2 July 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2031 KB, uploaded 3 July 2015]   |  

Abstract

A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset. This paper proposes a global unified data model (GUDM) to provide a global unified data structure for all data sources and generate a unified dataset by a “data modeler” tool. The proposed tool implements user-centric priority based approach which can easily resolve the problems of unified data modeling and overlapping attributes across multiple datasets. The tool is illustrated using sample diabetes mellitus data. The diverse data sources to generate the unified dataset for diabetes mellitus include clinical trial information, a social media interaction dataset and physical activity data collected using different sensors. To realize the significance of the unified dataset, we adopted a well-known rough set theory based rules creation process to create rules from the unified dataset. The evaluation of the tool on six different sets of locally created diverse datasets shows that the tool, on average, reduces 94.1% time efforts of the experts and knowledge engineer while creating unified datasets. View Full-Text
Keywords: unified dataset; data fusion; data model; rough set theory; knowledge acquisition; reasoning; clinical trials; social media; sensors unified dataset; data fusion; data model; rough set theory; knowledge acquisition; reasoning; clinical trials; social media; sensors
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ali, R.; Siddiqi, M.H.; Idris, M.; Ali, T.; Hussain, S.; Huh, E.-N.; Kang, B.H.; Lee, S. GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare. Sensors 2015, 15, 15772-15798.

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