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

Sensors

Rahman Ali¹  Muhammad Hameed Siddiqi¹  Muhammad Idris¹  Taqdir Ali¹  Shujaat Hussain¹  Eui-Nam Huh¹  Byeong Ho Kang²  Sungyoung Lee¹ 

[1] Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea; E-Mails:;Department of Computing and Information Systems, University of Tasmania, Hobart Tasmania 7005, Australia; E-Mail:

关键词: unified dataset;    data fusion;    data model;    rough set theory;    knowledge acquisition;    reasoning;    clinical trials;    social media;    sensors;   

DOI  :  10.3390/s150715772

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.