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|dc.identifier.citation||IEEE Transactions on Knowledge and Data Engineering, 2016; 28(9):2423-2437||-|
|dc.description.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.||-|
|dc.description.statementofresponsibility||Ling Chen, Xue Li, Quan Z. Sheng, Wen-Chih Peng, John Bennett, Hsiao-Yun Hu and Nicole Huang||-|
|dc.rights||© 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.||-|
|dc.subject||Health examination records; semi-supervised learning; heterogeneous graph extraction||-|
|dc.title||Mining health examination records - a graph-based approach||-|
|Appears in Collections:||Aurora harvest 7|
Computer Science publications
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