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

Yizhen Hai; Vincent Cc Cheng; Shui-Yee Wong; Kwok-Leung Tsui; Kwok-Yung Yuen

doi:10.1109/ISI.2011.5984112

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

Yizhen Hai, Vincent Cc Cheng, Shui-Yee Wong, Kwok-Leung Tsui, Kwok-Yung Yuen

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Statistical 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

Original language	English
Title of host publication	Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011
Pages	349-353
DOIs	https://doi.org/10.1109/ISI.2011.5984112
Publication status	Published - 2011
Event	2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011 - Beijing, China Duration: 10 Jul 2011 → 12 Jul 2011

Conference

Conference	2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011
Place	China
City	Beijing
Period	10/07/11 → 12/07/11

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Research Keywords

K-nearest Neighbour Algorithm
Logistic Regression
Methicillin-resistant Staphylococcus aureus (MRSA)
Prognostication

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10.1109/ISI.2011.5984112

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title = "Logistic regression analysis for predicting methicillin-resistant staphylococcus aureus (MRSA) in-hospital mortality",

abstract = "Statistical 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",

keywords = "K-nearest Neighbour Algorithm, Logistic Regression, Methicillin-resistant Staphylococcus aureus (MRSA), Prognostication",

author = "Yizhen Hai and Cheng, {Vincent Cc} and Shui-Yee Wong and Kwok-Leung Tsui and Kwok-Yung Yuen",

year = "2011",

doi = "10.1109/ISI.2011.5984112",

language = "English",

isbn = "9781457700828",

pages = "349--353",

booktitle = "Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011",

note = "2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011 ; Conference date: 10-07-2011 Through 12-07-2011",

}

Hai, Y, Cheng, VC, Wong, S-Y, Tsui, K-L & Yuen, K-Y 2011, Logistic regression analysis for predicting methicillin-resistant staphylococcus aureus (MRSA) in-hospital mortality. in Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011., 5984112, pp. 349-353, 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011, Beijing, China, 10/07/11. https://doi.org/10.1109/ISI.2011.5984112

Logistic regression analysis for predicting methicillin-resistant staphylococcus aureus (MRSA) in-hospital mortality. / Hai, Yizhen; Cheng, Vincent Cc; Wong, Shui-Yee et al.
Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011. 2011. p. 349-353 5984112.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Logistic regression analysis for predicting methicillin-resistant staphylococcus aureus (MRSA) in-hospital mortality

AU - Hai, Yizhen

AU - Cheng, Vincent Cc

AU - Wong, Shui-Yee

AU - Tsui, Kwok-Leung

AU - Yuen, Kwok-Yung

PY - 2011

Y1 - 2011

N2 - Statistical 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

AB - Statistical 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

KW - K-nearest Neighbour Algorithm

KW - Logistic Regression

KW - Methicillin-resistant Staphylococcus aureus (MRSA)

KW - Prognostication

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DO - 10.1109/ISI.2011.5984112

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781457700828

SP - 349

EP - 353

BT - Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011

T2 - 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011

Y2 - 10 July 2011 through 12 July 2011

ER -

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

Abstract

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UN SDGs

Research Keywords

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