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Conference Paper: Logistic regression analysis for predicting methicillin-resistant staphylococcus aureus (MRSA) in-hospital mortality

TitleLogistic regression analysis for predicting methicillin-resistant staphylococcus aureus (MRSA) in-hospital mortality
Authors
KeywordsK-Nearest Neighbour Algorithm
Logistic Regression
Methicillin-Resistant Staphylococcus Aureus (Mrsa)
Prognostication
Issue Date2011
Citation
Proceedings Of 2011 Ieee International Conference On Intelligence And Security Informatics, Isi 2011, 2011, p. 349-353 How to Cite?
AbstractStatistical models have been widely used in public health and made a difference in a wide range of applications. For example, they provide new ideas for efficient feature selection. This paper attempts to demonstrate how to apply regression-based methods to accurately predict in-hospital mortality of Methicillin-resistant Staphylococcus Aureus (MRSA) patients. Logistic regression is used to predict the in-hospital death. It is found that admission age, residency, solid tumor, hemic malignancy, COAD, Dementia, PLT, Lymphocyte, Urea, and ALP are the significant prognostic factors (P<0.1) for in-hospital survival. Using cross validation and random splitting and the prediction accuracy is around 85%. The future research direction is to strengthen the robustness of the predictive model. Possible direction is to make use of other data mining "blackbox" methods, such as k-NN and SVM. These models also need further validation on their performance and feature selection. © 2011 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/159064
References

 

DC FieldValueLanguage
dc.contributor.authorHai, Yen_US
dc.contributor.authorCheng, VCen_US
dc.contributor.authorWong, SYen_US
dc.contributor.authorTsui, KLen_US
dc.contributor.authorYuen, KYen_US
dc.date.accessioned2012-08-08T09:06:10Z-
dc.date.available2012-08-08T09:06:10Z-
dc.date.issued2011en_US
dc.identifier.citationProceedings Of 2011 Ieee International Conference On Intelligence And Security Informatics, Isi 2011, 2011, p. 349-353en_US
dc.identifier.urihttp://hdl.handle.net/10722/159064-
dc.description.abstractStatistical models have been widely used in public health and made a difference in a wide range of applications. For example, they provide new ideas for efficient feature selection. This paper attempts to demonstrate how to apply regression-based methods to accurately predict in-hospital mortality of Methicillin-resistant Staphylococcus Aureus (MRSA) patients. Logistic regression is used to predict the in-hospital death. It is found that admission age, residency, solid tumor, hemic malignancy, COAD, Dementia, PLT, Lymphocyte, Urea, and ALP are the significant prognostic factors (P<0.1) for in-hospital survival. Using cross validation and random splitting and the prediction accuracy is around 85%. The future research direction is to strengthen the robustness of the predictive model. Possible direction is to make use of other data mining "blackbox" methods, such as k-NN and SVM. These models also need further validation on their performance and feature selection. © 2011 IEEE.en_US
dc.languageengen_US
dc.relation.ispartofProceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011en_US
dc.subjectK-Nearest Neighbour Algorithmen_US
dc.subjectLogistic Regressionen_US
dc.subjectMethicillin-Resistant Staphylococcus Aureus (Mrsa)en_US
dc.subjectPrognosticationen_US
dc.titleLogistic regression analysis for predicting methicillin-resistant staphylococcus aureus (MRSA) in-hospital mortalityen_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/ISI.2011.5984112en_US
dc.identifier.scopuseid_2-s2.0-80052883922en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80052883922&selection=ref&src=s&origin=recordpageen_US
dc.identifier.spage349en_US
dc.identifier.epage353en_US
dc.identifier.scopusauthoridHai, Y=44861107400en_US
dc.identifier.scopusauthoridCheng, VC=38662328400en_US
dc.identifier.scopusauthoridWong, SY=7404590879en_US
dc.identifier.scopusauthoridTsui, KL=7101671584en_US
dc.identifier.scopusauthoridYuen, KY=36078079100en_US

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