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Comput Biol Med. 2014 Apr;47:13-9. doi: 10.1016/j.compbiomed.2013.12.012. Epub 2014 Jan 15.

Prediction of survival of ICU patients using computational intelligence.

Author information

1
Department of Computer Science & Information Engineering, National Central University, Taiwan.
2
Department of Computer Science & Information Engineering, National Central University, Taiwan. Electronic address: muchun@csie.ncu.edu.tw.
3
Medical Intensive Care Unit, Cathay General Hospital, Taiwan.
4
Quality Management Center, Cathay General Hospital, Taiwan.

Abstract

This paper presents a computational-intelligence-based model to predict the survival rate of critically ill patients who were admitted to an intensive care unit (ICU). The prediction input variables were based on the first 24 h admission physiological data of ICU patients to forecast whether the final outcome was survival or not. The prediction model was based on a particle swarm optimization (PSO)-based Fuzzy Hyper-Rectangular Composite Neural Network (PFHRCNN) that integrates three computational intelligence tools including hyper-rectangular composite neural networks, fuzzy systems and PSO. It could help doctors to make appropriate treatment decisions without excessive laboratory tests. The performance of the proposed prediction model was evaluated on the data set collected from 300 ICU patients in the Cathy General Hospital in 2012. There were 10 input variables in total for the prediction model. Nine of these variables (e.g. systolic arterial blood pressures, systolic non-invasive blood pressures, respiratory rate, heart rate, and body temperature) were routinely available for 24 h in ICU and the last variable is patient's age. The proposed model could achieve a 96% and 86% accuracy rate for the training data and testing data, respectively.

KEYWORDS:

Clinical management; Computational intelligence; Fuzzy systems; ICU; Neural networks; Prediction of survival rate

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