Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6948475 | Decision Support Systems | 2016 | 36 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Information Systems
Authors
Ling Chen, Xue Li, Yi Yang, Hanna Kurniawati, Quan Z. Sheng, Hsiao-Yun Hu, Nicole Huang,