Data Mining on ICU Mortality Prediction Using Early Temporal Data: A Survey

Jianfeng Xu, Yuanjian Zhang, Peng Zhang (), Azhar Mahmood (), Yu Li () and Shaheen Khatoon ()
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Jianfeng Xu: School of Software Engineering, Nanchang University, Nanchang, Jiangxi 330047, P. R. China2School of Electronics and Information Engineering, Tongji University, Shanghai 201804, P. R. China
Yuanjian Zhang: School of Electronics and Information Engineering, Tongji University, Shanghai 201804, P. R. China
Peng Zhang: Center on Quantum Computation & Intelligent Systems, University of Technology Sydney, Ultimo, NSW 2007, Australia
Azhar Mahmood: Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institue of Science & Technology-SZABIST, Islamabad 44000, Pakistan
Yu Li: North Automatic Control Technology Institute, Taiyuan, Shanxi 030006, P. R. China
Shaheen Khatoon: College of Computer Science and Information Technology, Department of Information System, King Faisal University (KFU), Al-Ahsaa, 31982, Saudi Arabia

International Journal of Information Technology & Decision Making (IJITDM), 2017, vol. 16, issue 01, 117-159

Abstract: Predicting mortality rate for the patients in Intensive Care Unit (ICU) is an active topic in medical domain for decades. The main goal of mortality prediction is to achieve satisfied discrimination and calibration. However, the particular features of the patient records such as high-dimension, irregular, and imbalance nature of ICU data makes prediction challenging. Data mining is gaining an ever-increasing popularity in predicting mortality of ICU patients recently, a comprehensive literature review of the subject has yet to be carried out. This study presented a review of and classification scheme for the past research as well as latest progress and their limitations on application of data mining techniques for predicting ICU mortality. Based on limitations, a hybrid framework combined with intrinsic property of ICU data to improve prediction performance is proposed for future research.

Keywords: ICU; mortality prediction; time series; data mining; hybrid framework (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219622016300020

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