Abstract
OBJECTIVES: The intensive care environment generates a wealth of critical care data suited to developing a well-calibrated prediction tool. This study was done to develop an intensive care unit (ICU) mortality prediction model built on University of Kentucky Hospital (UKH)'s data and to assess whether the performance of various data mining techniques, such as the artificial neural network (ANN), support vector machine (SVM) and decision trees (DT), outperform the conventional logistic regression (LR) statistical model.
METHODS: The models were built on ICU data collected regarding 38,474 admissions to the UKH between January 1998 and September 2007. The first 24 hours of the ICU admission data were used, including patient demographics, admission information, physiology data, chronic health items, and outcome information.
RESULTS: Only 15 study variables were identified as significant for inclusion in the model development. The DT algorithm slightly outperformed (AUC, 0.892) the other data mining techniques, followed by the ANN (AUC, 0.874), and SVM (AUC, 0.876), compared to that of the APACHE III performance (AUC, 0.871).
CONCLUSIONS: With fewer variables needed, the machine learning algorithms that we developed were proven to be as good as the conventional APACHE III prediction.
Document Type
Article
Publication Date
12-2011
Digital Object Identifier (DOI)
https://doi.org/10.4258/hir.2011.17.4.232
Funding Information
This publication was made possible by Grant Number P20 RR16481 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH).
Repository Citation
Kim, Sujin; Kim, Woojae; and Park, Rae Woong, "A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques" (2011). Institute for Biomedical Informatics Faculty Publications. 17.
https://uknowledge.uky.edu/bmi_facpub/17
Notes/Citation Information
Published in Healthcare Informatics Research, v. 17, issue 4.
© 2011 The Korean Society of Medical Informatics
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.