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GUDM: automatic generation of unified datasets for learning and reasoning in healthcare

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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, S
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.

History

Publication title

Sensors

Volume

15

Issue

7

Pagination

15772-15798

ISSN

1424-8220

Department/School

School of Information and Communication Technology

Publisher

Molecular Diversity Preservation International

Place of publication

Matthaeusstrasse 11, Basel, Switzerland, Ch-4057

Rights 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

Socio-economic Objectives

Information services not elsewhere classified

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