Mining health examination records - a graph-based approach

Chen, Ling, Li, Xue, Sheng, Quan Z, Peng, Wen-Chih, Bennett, John, Hu, Hsiao-Yun and Huang, Nicole (2016) Mining health examination records - a graph-based approach. IEEE Transactions on Knowledge and Data Engineering, 28 9: 2423-2437. doi:10.1109/TKDE.2016.2561278

Author Chen, Ling
Li, Xue
Sheng, Quan Z
Peng, Wen-Chih
Bennett, John
Hu, Hsiao-Yun
Huang, Nicole
Title Mining health examination records - a graph-based approach
Journal name IEEE Transactions on Knowledge and Data Engineering   Check publisher's open access policy
ISSN 1041-4347
Publication date 2016-09-01
Sub-type Article (original research)
DOI 10.1109/TKDE.2016.2561278
Open Access Status Not yet assessed
Volume 28
Issue 9
Start page 2423
End page 2437
Total pages 15
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Subject 1710 Information Systems
1706 Computer Science Applications
1703 Computational Theory and Mathematics
Abstract General health examination is an integral part of healthcare in many countries. Identifying the participants at risk is important for early warning and preventive intervention. The fundamental challenge of learning a classification model for risk prediction lies in the unlabeled data that constitutes the majority of the collected dataset. Particularly, the unlabeled data describes the participants in health examinations whose health conditions can vary greatly from healthy to very-ill. There is no ground truth for differentiating their states of health. In this paper, we propose a graph-based, semi-supervised learning algorithm called SHG-Health (Semi-supervised Heterogeneous Graph on Health) for risk predictions to classify a progressively developing situation with the majority of the data unlabeled. An efficient iterative algorithm is designed and the proof of convergence is given. Extensive experiments based on both real health examination datasets and synthetic datasets are performed to show the effectiveness and efficiency of our method.
Keyword Health examination records
Heterogeneous graph extraction
Semi-supervised learning
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
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