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

Ling Chen*, Xue Li, Yi Yang, Hanna Kurniawati, Quan Z. Sheng, Hsiao Yun Hu, Nicole Huang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)54-65
Number of pages12
JournalDecision Support Systems
Publication statusPublished - 1 Jan 2016
Externally publishedYes


  • Data mining
  • Feature extraction
  • Geriatric medical examination
  • Label uncertainty
  • Personal health index


Dive into the research topics of 'Personal health indexing based on medical examinations: A data mining approach'. Together they form a unique fingerprint.

Cite this