Mining Personal Health Index from annual geriatric medical examinations

Ling Chen, Xue Li, Sen Wang, Hsiao-Yun Hu, Nicole Huang, Quan Z. Sheng, Mohamed Sharaf

Research output: Contribution to journalConference paper

9 Citations (Scopus)

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.

Original languageEnglish
Article number7023397
Pages (from-to)761-766
Number of pages6
JournalProceedings - IEEE International Conference on Data Mining, ICDM
DOIs
Publication statusPublished - 2014
Externally publishedYes
EventIEEE International Conference on Data Mining (14th : 2014) - Shenzhen, China
Duration: 14 Dec 201417 Dec 2014

Keywords

  • geriatric medical examination
  • Personal Health Index

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