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GUDM: automatic generation of unified datasets for learning and reasoning in healthcare
journal contribution
posted on 2023-05-18, 17:42 authored by Ali, R, Siddiqi, MH, Ahmed, MI, Ali, T, Hussain, S, Huh, EN, Byeong KangByeong Kang, Lee, SA 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.
History
Publication title
SensorsVolume
15Issue
7Pagination
15772-15798ISSN
1424-8220Department/School
School of Information and Communication TechnologyPublisher
Molecular Diversity Preservation InternationalPlace of publication
Matthaeusstrasse 11, Basel, Switzerland, Ch-4057Rights statement
Copyright 2015 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/Repository Status
- Open