title: GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare.
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.
authors.affiliation: Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. rahmanali@oslab.khu.ac.kr.
authors.forename: Rahman
authors.initials: R
authors.lastname: Ali
authors.affiliation: Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. siddiqi@oslab.khu.ac.kr.
authors.forename: Muhammad Hameed
authors.initials: MH
authors.lastname: Siddiqi
authors.affiliation: Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. idris@oslab.khu.ac.kr.
authors.forename: Muhammad
authors.initials: M
authors.lastname: Idris
authors.affiliation: Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. taqdir.ali@oslab.khu.ac.kr.
authors.forename: Taqdir
authors.initials: T
authors.lastname: Ali
authors.affiliation: Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. shujaat.hussain@oslab.khu.ac.kr.
authors.forename: Shujaat
authors.initials: S
authors.lastname: Hussain
authors.affiliation: Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. johnhuh@khu.ac.kr.
authors.forename: Eui-Nam
authors.initials: EN
authors.lastname: Huh
authors.affiliation: Department of Computing and Information Systems, University of Tasmania, Hobart Tasmania 7005, Australia. byeong.kang@utas.edu.au.
authors.forename: Byeong Ho
authors.initials: BH
authors.lastname: Kang
authors.affiliation: Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. sylee@oslab.khu.ac.kr.
authors.forename: Sungyoung
authors.initials: S
authors.lastname: Lee
mesh_terms: D002986:Clinical Trials as Topic; D003628:Database Management Systems; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008491:Medical Informatics Applications; D061108:Social Media
keywords: clinical trials; data fusion; data model; knowledge acquisition; reasoning; rough set theory; sensors; social media; unified dataset