Journal
DECISION SUPPORT SYSTEMS
Volume 81, Issue -, Pages 54-65Publisher
ELSEVIER
DOI: 10.1016/j.dss.2015.10.008
Keywords
Personal health index; Geriatric medical examination; Label uncertainty; Data mining; Feature extraction
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Funding
- Australian Research Council Discovery Project [DP140100104]
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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. (C) 2015 Elsevier B.V. All rights reserved.
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