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Prediction of acute hypotensive episodes using EMD, statistical method and multi GP

Prediction of acute hypotensive episodes using EMD, statistical method and multi GP Acute hypotensive episodes (AHE) is a critical event in the ICU as it can cause multiple organ failure and thus has severe implications on the mortality risk. Timely and rapid intervention is necessary to save a patient’s life. For the sake of this purpose, this paper presents a methodology to predict AHE for ICU patients based on mean arterial pressure time series. First, the empirical mode decomposition method is used to calculate complex MAP series. Then the original and intrinsic mode functions of MAP series are transformed for the feature extraction with the methods of probabilistic distribution and statistics. Finally, the multi genetic programming is presented to build the classification models for the prediction of AHE. The methodology is applied to the dataset from Multi-parameter intelligent monitoring for intensive care. The achieved accuracy of the proposed methodology is 82.92 % in the training set and 79.93 % in the testing set with the sixfold cross-validation in the total 2866 patient’s records. Simulation results revealed the reasonable forecasting accuracy in prediction of AHE in 1 h forecasting window. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Soft Computing Springer Journals

Prediction of acute hypotensive episodes using EMD, statistical method and multi GP

Jiang, Dazhi; Hu, Bo; Wu, Zhijian
Soft Computing , Volume 21 (17) – Mar 10, 2016
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References (29)

Publisher
Springer Journals
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Mathematical Logic and Foundations; Control, Robotics, Mechatronics
ISSN
1432-7643
eISSN
1433-7479
DOI
10.1007/s00500-016-2107-0
Publisher site
See Article on Publisher Site

Abstract

Acute hypotensive episodes (AHE) is a critical event in the ICU as it can cause multiple organ failure and thus has severe implications on the mortality risk. Timely and rapid intervention is necessary to save a patient’s life. For the sake of this purpose, this paper presents a methodology to predict AHE for ICU patients based on mean arterial pressure time series. First, the empirical mode decomposition method is used to calculate complex MAP series. Then the original and intrinsic mode functions of MAP series are transformed for the feature extraction with the methods of probabilistic distribution and statistics. Finally, the multi genetic programming is presented to build the classification models for the prediction of AHE. The methodology is applied to the dataset from Multi-parameter intelligent monitoring for intensive care. The achieved accuracy of the proposed methodology is 82.92 % in the training set and 79.93 % in the testing set with the sixfold cross-validation in the total 2866 patient’s records. Simulation results revealed the reasonable forecasting accuracy in prediction of AHE in 1 h forecasting window.

Journal

Soft Computing – Springer Journals

Published: Mar 10, 2016

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