Personal health indexing based on medical examinations: a data mining approach

Chen, Ling, Li, Xue, Yang, Yi, Kurniawati, Hanna, Sheng, Quan Z., Hu, Hsiao-Yun and Huang, Nicole (2015) Personal health indexing based on medical examinations: a data mining approach. Decision Support Systems, 81 54-65. doi:10.1016/j.dss.2015.10.008

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Author Chen, Ling
Li, Xue
Yang, Yi
Kurniawati, Hanna
Sheng, Quan Z.
Hu, Hsiao-Yun
Huang, Nicole
Title Personal health indexing based on medical examinations: a data mining approach
Journal name Decision Support Systems   Check publisher's open access policy
ISSN 0167-9236
Publication date 2015-02-05
Sub-type Article (original research)
DOI 10.1016/j.dss.2015.10.008
Open Access Status Not Open Access
Volume 81
Start page 54
End page 65
Total pages 12
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Subject 1404 Management Information Systems
1710 Information Systems
3204 Developmental and Educational Psychology
1201 Arts and Humanities (miscellaneous)
1802 Information Systems and Management
Abstract We design a method called MyPHI that predicts personal health index (PHI), a new evidence-based health indicator to explore the underlying patterns of a large collection of geriatric medical examination (GME) records using data mining techniques. We define PHI as a vector of scores, each reflecting the health risk in a particular disease category. The PHI prediction is formulated as an optimization problem that finds the optimal soft labels as health scores based on medical records that are infrequent, incomplete, and sparse. Our method is compared with classification models commonly used in medical applications. The experimental evaluation has demonstrated the effectiveness of our method based on a real-world GME data set collected from 102,258 participants.
Keyword Data mining
Feature extraction
Geriatric medical examination
Label uncertainty
Personal health index
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID DP140100104
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 7 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 8 times in Scopus Article | Citations
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