Data Mining on ICU Mortality Prediction Using Early Temporal Data: A Survey

Xu, J; Zhang, Y; Zhang, P; Mahmood, A; Li, Y; Khatoon, S

Data Mining on ICU Mortality Prediction Using Early Temporal Data: A Survey

Xu, J Zhang, Y Zhang, P

Mahmood, A Li, Y Khatoon, S

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Publication Type:: Journal Article
Citation:: International Journal of Information Technology and Decision Making, 2017, 16 (1), pp. 117 - 159
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Xu, J	en_US
dc.contributor.author	Zhang, Y	en_US
dc.contributor.author	Zhang, P https://orcid.org/0000-0001-7973-2746	en_US
dc.contributor.author	Mahmood, A	en_US
dc.contributor.author	Li, Y	en_US
dc.contributor.author	Khatoon, S	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	International Journal of Information Technology and Decision Making, 2017, 16 (1), pp. 117 - 159	en_US
dc.identifier.issn	0219-6220	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127419
dc.description.abstract	© 2017 World Scientific Publishing Company. Predicting mortality rate for the patients in Intensive Care Unit (ICU) is an active topic in medical domain for decades. The main goal of mortality prediction is to achieve satisfied discrimination and calibration. However, the particular features of the patient records such as high-dimension, irregular, and imbalance nature of ICU data makes prediction challenging. Data mining is gaining an ever-increasing popularity in predicting mortality of ICU patients recently, a comprehensive literature review of the subject has yet to be carried out. This study presented a review of and classification scheme for the past research as well as latest progress and their limitations on application of data mining techniques for predicting ICU mortality. Based on limitations, a hybrid framework combined with intrinsic property of ICU data to improve prediction performance is proposed for future research.	en_US
dc.relation.ispartof	International Journal of Information Technology and Decision Making	en_US
dc.relation.isbasedon	10.1142/S0219622016300020	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Data Mining on ICU Mortality Prediction Using Early Temporal Data: A Survey	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	16	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1503 Business and Management	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
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	16	en_US

Abstract:

© 2017 World Scientific Publishing Company. Predicting mortality rate for the patients in Intensive Care Unit (ICU) is an active topic in medical domain for decades. The main goal of mortality prediction is to achieve satisfied discrimination and calibration. However, the particular features of the patient records such as high-dimension, irregular, and imbalance nature of ICU data makes prediction challenging. Data mining is gaining an ever-increasing popularity in predicting mortality of ICU patients recently, a comprehensive literature review of the subject has yet to be carried out. This study presented a review of and classification scheme for the past research as well as latest progress and their limitations on application of data mining techniques for predicting ICU mortality. Based on limitations, a hybrid framework combined with intrinsic property of ICU data to improve prediction performance is proposed for future research.

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http://hdl.handle.net/10453/127419