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Open Access Research article

A novel time series analysis approach for prediction of dialysis in critically ill patients using echo-state networks

T Verplancke1*, S Van Looy2, K Steurbaut2, D Benoit1, F De Turck2, G De Moor3 and J Decruyenaere1

Author Affiliations

1 Department of Intensive Care Medicine, Ghent University Hospital, Faculty of Medicine, Ghent University, Ghent, Belgium

2 Department of Information Technology, Faculty of Engineering, Ghent University, Ghent, Belgium

3 Department of Medical Informatics and Statistics, Faculty of Medicine, Ghent University, Ghent, Belgium

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BMC Medical Informatics and Decision Making 2010, 10:4  doi:10.1186/1472-6947-10-4

Published: 21 January 2010

Abstract

Background

Echo-state networks (ESN) are part of a group of reservoir computing methods and are basically a form of recurrent artificial neural networks (ANN). These methods can perform classification tasks on time series data. The recurrent ANN of an echo-state network has an 'echo-state' characteristic. This 'echo-state' functions as a fading memory: samples that have been introduced into the network in a further past, are faded away. The echo-state approach for the training of recurrent neural networks was first described by Jaeger H. et al. In clinical medicine, until this moment, no original research articles have been published to examine the use of echo-state networks.

Methods

This study examines the possibility of using an echo-state network for prediction of dialysis in the ICU. Therefore, diuresis values and creatinine levels of the first three days after ICU admission were collected from 830 patients admitted to the intensive care unit (ICU) between May 31th 2003 and November 17th 2007. The outcome parameter was the performance by the echo-state network in predicting the need for dialysis between day 5 and day 10 of ICU admission. Patients with an ICU length of stay <10 days or patients that received dialysis in the first five days of ICU admission were excluded. Performance by the echo-state network was then compared by means of the area under the receiver operating characteristic curve (AUC) with results obtained by two other time series analysis methods by means of a support vector machine (SVM) and a naive Bayes algorithm (NB).

Results

The AUC's in the three developed echo-state networks were 0.822, 0.818, and 0.817. These results were comparable to the results obtained by the SVM and the NB algorithm.

Conclusions

This proof of concept study is the first to evaluate the performance of echo-state networks in an ICU environment. This echo-state network predicted the need for dialysis in ICU patients. The AUC's of the echo-state networks were good and comparable to the performance of other classification algorithms. Moreover, the echo-state network was more easily configured than other time series modeling technologies.