Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/110475
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Type: Conference paper
Title: Mining Personal Health Index from annual geriatric medical examinations
Author: Chen, L.
Li, X.
Wang, S.
Hu, H.
Huang, N.
Sheng, Q.
Sharif, M.
Citation: Proceedings: 14th IEEE International Conference on Data Mining (ICDM 2014), 2014 / Kumar, R., Toivonen, H., Pei, J., Huang, J., Wu, X. (ed./s), vol.2015-January, iss.January, pp.761-766
Publisher: IEEE
Issue Date: 2014
Series/Report no.: IEEE International Conference on Data Mining
ISBN: 9781479943029
ISSN: 1550-4786
2374-8486
Conference Name: 14th IEEE International Conference on Data Mining (ICDM 2014) (14 Dec 2014 - 17 Dec 2014 : Shenzhen, China)
Statement of
Responsibility: 
Ling Chen, Xue Li, Sen Wang, Hsiao-Yun Hu, Nicole Huang, Quan Z. Sheng, and Mohamed Sharaf
Abstract: People take regular medical examinations mostly not for discovering diseases but for having a peace of mind regarding their health status. Therefore, it is important to give them an overall feedback with respect to all the health indicators that have been ranked against the whole population. In this paper, we propose a framework of mining Personal Health Index (PHI) from a large and comprehensive geriatric medical examination (GME) dataset. We define PHI as an overall score of personal health status based on a complement probability of health risks. The health risks are calculated using the information from the cause of death (COD) dataset that is linked to the GME dataset. Especially, the highest health risk is revealed in the cases of people who had been taking GME for some years and then passed away for medical reasons. The proposed framework consists of methods in data pre-processing, feature extraction and selection, and model selection. The effectiveness of the proposed framework is validated by a set of comprehensive experiments based on the records of 102,258 participants. As the first of this kind, our work provides a baseline for further research.
Rights: © 2014 IEEE
RMID: 0030027217
DOI: 10.1109/ICDM.2014.32
Grant ID: http://purl.org/au-research/grants/arc/DP140100104
Appears in Collections:Computer Science publications

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