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Conference Paper: Prognostication of Methicillin-resistant Staphylococcus Aureus (MRSA) patient survival

TitlePrognostication of Methicillin-resistant Staphylococcus Aureus (MRSA) patient survival
Authors
KeywordsCox Proportional Hazdard Model
Methicillin-Resistant Staphylococcus Aureus (Mrsa)
Multivariate Survival Analysis
Prognostication
Reliability Theory
Issue Date2011
Citation
2011 Prognostics And System Health Management Conference, Phm-Shenzhen 2011, 2011 How to Cite?
AbstractPrognostic methods are potentially beneficial for public health management. The blending of data-driven methods with the domain knowledge is essential to efficiently advance feature selection, anomaly detection, prognostics forecasting, data matching and clustering. This paper attempts to demonstrate how prognostic methods enable accurate Methicillin-resistant Staphylococcus Aureus (MRSA) patient life prediction. The methodology is applied to MRSA patient survival analysis. Significant linear relationship is found between log (hazard) and age (p<#60;0.001). By adjusting the time-depending effect of age, we construct more accurate Cox's proportional hazard models. It is believed that understanding age effect on MRSA patient survival is able to receive more robust result using prognostic approaches. To further enhance model prediction power, it is suggested to explore statistical data transformation and adjustment under various attributes. © 2011 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/159063
References

 

DC FieldValueLanguage
dc.contributor.authorWong, SYen_US
dc.contributor.authorHai, Yen_US
dc.contributor.authorCheng, VCCen_US
dc.contributor.authorYuen, KYen_US
dc.contributor.authorTsui, KLen_US
dc.date.accessioned2012-08-08T09:06:09Z-
dc.date.available2012-08-08T09:06:09Z-
dc.date.issued2011en_US
dc.identifier.citation2011 Prognostics And System Health Management Conference, Phm-Shenzhen 2011, 2011en_US
dc.identifier.urihttp://hdl.handle.net/10722/159063-
dc.description.abstractPrognostic methods are potentially beneficial for public health management. The blending of data-driven methods with the domain knowledge is essential to efficiently advance feature selection, anomaly detection, prognostics forecasting, data matching and clustering. This paper attempts to demonstrate how prognostic methods enable accurate Methicillin-resistant Staphylococcus Aureus (MRSA) patient life prediction. The methodology is applied to MRSA patient survival analysis. Significant linear relationship is found between log (hazard) and age (p<#60;0.001). By adjusting the time-depending effect of age, we construct more accurate Cox's proportional hazard models. It is believed that understanding age effect on MRSA patient survival is able to receive more robust result using prognostic approaches. To further enhance model prediction power, it is suggested to explore statistical data transformation and adjustment under various attributes. © 2011 IEEE.en_US
dc.languageengen_US
dc.relation.ispartof2011 Prognostics and System Health Management Conference, PHM-Shenzhen 2011en_US
dc.subjectCox Proportional Hazdard Modelen_US
dc.subjectMethicillin-Resistant Staphylococcus Aureus (Mrsa)en_US
dc.subjectMultivariate Survival Analysisen_US
dc.subjectPrognosticationen_US
dc.subjectReliability Theoryen_US
dc.titlePrognostication of Methicillin-resistant Staphylococcus Aureus (MRSA) patient survivalen_US
dc.typeConference_Paperen_US
dc.identifier.emailYuen, KY:kyyuen@hkucc.hku.hken_US
dc.identifier.authorityYuen, KY=rp00366en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/PHM.2011.5939586en_US
dc.identifier.scopuseid_2-s2.0-79960896089en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79960896089&selection=ref&src=s&origin=recordpageen_US
dc.identifier.scopusauthoridWong, SY=7404590879en_US
dc.identifier.scopusauthoridHai, Y=44861107400en_US
dc.identifier.scopusauthoridCheng, VCC=38662328400en_US
dc.identifier.scopusauthoridYuen, KY=36078079100en_US
dc.identifier.scopusauthoridTsui, KL=7101671584en_US

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