Authors: Lili Chen; Xi Zhang; Xiaoyun Xu; Liang Zhao
Addresses: Department of Industrial Engineering and Management, Peking University, Beijing 100871, China ' Department of Industrial Engineering and Management, Peking University, Beijing 100871, China ' Department of Industrial Engineering and Management, Peking University, Beijing 100871, China ' Peking University Third Hospital, Peking University, Beijing 100871, China
Abstract: Due to the different health conditions of an increasing number of serious patients, the Intensive Care Unit (ICU) of a hospital has to correctly classify patients according to their conditions so that medical resources could be properly utilised. The seriousness of the illness can be classified based on the significant risk factors and its corresponding impacts on the patients' survival. How to quickly identify the significant variables is a major task for classification. This paper proposes a Multistage-EDA-Enhanced Logistic Regression (MEDAeLR) approach to precisely classify the patients and quickly diagnose with three-stage analysis. A cohort of 200 consecutive ICU patients was borrowed for validation. Regular MLR, classification trees and Linear Discriminant Analysis (LDA) are carried to compare the performance with proposed method. The results show that MEDAeLR provides more satisfactory identification performance in terms of Receiver Operating Characteristic (ROC) curve and Area under the ROC Curve (AUC).
Keywords: ICU data; patient classification; MEDAeLR; mortality prediction; intensive care units; hospitals; EDA-enhanced logistic regression; modelling; serious illness; medical diagnosis; patient deaths; exploratory data analysis.
International Journal of Services Operations and Informatics, 2012 Vol.7 No.2/3, pp.182 - 196
Available online: 30 Dec 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article