Mining Health Examination Records-A Graph-Based Approach

Ling Chen, Xue Li, Quan Z. Sheng, Wen Chih Peng, John Bennett, Hsiao Yun Hu, Nicole Huang

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


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.

Original languageEnglish
Article number7463501
Pages (from-to)2423-2437
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number9
Publication statusPublished - 1 Sept 2016
Externally publishedYes


  • Health examination records
  • heterogeneous graph extraction
  • semi-supervised learning


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