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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51485

Title: Prediction of Survival of ICU Patients using Computational Intelligence
Authors: Y. Z. Hsieh
Mu-Chun Su
Chen-Hsu Wang
Pa-Chun Wang
Contributors: 國立臺灣海洋大學:電機工程學系
Keywords: ICU
Prediction of survival rate
Computational intelligence
Neural networks
Fuzzy systems
Clinical management
Date: 2014-04
Issue Date: 2018-11-30T08:08:12Z
Publisher: Computers in Biology and Medicine
Abstract: 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.
Relation: 47 pp.13-19
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51485
Appears in Collections:[電機工程學系] 期刊論文

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