TY - GEN
T1 - Prognostication of Methicillin-resistant Staphylococcus Aureus (MRSA) patient survival
AU - Wong, Shui Yee
AU - Hai, Yizhen
AU - Cheng, Vincent C.C.
AU - Yuen, Kwok Yung
AU - Tsui, Kwok Leung
PY - 2011
Y1 - 2011
N2 - Prognostic 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.
AB - Prognostic 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.
KW - Cox Proportional Hazdard Model
KW - Methicillin-resistant Staphylococcus aureus (MRSA)
KW - Multivariate Survival Analysis
KW - Prognostication
KW - Reliability Theory
UR - http://www.scopus.com/inward/record.url?scp=79960896089&partnerID=8YFLogxK
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U2 - 10.1109/PHM.2011.5939586
DO - 10.1109/PHM.2011.5939586
M3 - Conference contribution
AN - SCOPUS:79960896089
SN - 9781424479511
T3 - 2011 Prognostics and System Health Management Conference, PHM-Shenzhen 2011
BT - 2011 Prognostics and System Health Management Conference, PHM-Shenzhen 2011
T2 - 2011 Prognostics and System Health Management Conference, PHM-Shenzhen 2011
Y2 - 24 May 2011 through 25 May 2011
ER -