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

Chen, L; Li, X; Yang, Y; Kurniawati, H; Sheng, QZ; Hu, HY; Huang, N

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

Chen, L Li, X Yang, Y

Kurniawati, H Sheng, QZ Hu, HY Huang, N

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Publication Type:: Journal Article
Citation:: Decision Support Systems, 2016, 81 pp. 54 - 65
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Chen, L	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Kurniawati, H	en_US
dc.contributor.author	Sheng, QZ	en_US
dc.contributor.author	Hu, HY	en_US
dc.contributor.author	Huang, N	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Decision Support Systems, 2016, 81 pp. 54 - 65	en_US
dc.identifier.issn	0167-9236	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121753
dc.description.abstract	© 2015 Elsevier B.V. All rights reserved. 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.	en_US
dc.relation	http://purl.org/au-research/grants/arc/LP160100630
dc.relation.ispartof	Decision Support Systems	en_US
dc.relation.isbasedon	10.1016/j.dss.2015.10.008	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Personal health indexing based on medical examinations: A data mining approach	en_US
dc.type	Journal Article
utslib.citation.volume	81	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	81	en_US

Abstract:

© 2015 Elsevier B.V. All rights reserved. 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.

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http://hdl.handle.net/10453/121753