Time series classification for the prediction of dialysis in critically ill patients using echo statenetworks

Objective: Time series often appear in medical databases, but only few machine learning methods exist that process this kind of data properly. Most modeling techniques have been designed with a static data model in mind and are not suitable for coping with the dynamic nature of time series. Recurrent Neural Networks (RNN) are often used to process time series, but only a few training algorithms exist for RNNs which are complex and often yield poor results. Therefore, researchers often turn to traditional machine ∗Corresponding author: Tel.: +32 9 331 49 38, Fax: +32 9 331 48 99 Email addresses: [email protected] (Femke Ongenae), [email protected] (Stijn Van Looy), [email protected] (David Verstraeten), [email protected] (Thierry Verplancke), [email protected] (Dominique Benoit), [email protected] (Filip De Turck), [email protected] (Tom Dhaene), [email protected] (Benjamin Schrauwen), [email protected] (Johan Decruyenaere) Preprint submitted to Engineering Applications of Artificial Intelligence July 25, 2012 learning approaches, such as support vector machines (SVM), which can easily be set up and trained and combine them with feature extraction (FE) and selection (FS) to process the high-dimensional temporal data. Recently, a new approach, called echo state networks (ESN), has been developed to simplify the training process of RNNs. This approach allows modeling the dynamics of a system based on time series data in a straightforward way. The objective of this study is to explore the advantages of using ESN instead of other traditional classifiers combined with FE and FS in classification problems in the intensive care unit (ICU) when the input data consists of time series. While ESNs have mostly been used to predict the future course of a time series, we use the ESN model for classification instead. Although time series often appear in medical data, little medical applications of ESNs have been studied yet. Methods and material: ESN is used to predict the need for dialysis between the fifth and tenth day after admission in the ICU. The input time series consist of measured diuresis and creatinine values during the first 3 days after admission. Data about 830 patients was used for the study, of which 82 needed dialysis between the fifth and tenth day after admission. ESN is compared to traditional classifiers, a sophisticated and a simple one, namely support vector machines and the naive Bayes (NB) classifier. Prior to the use of the SVM and NB classifier, FE and FS is required to reduce the number of input features and thus alleviate the curse dimensionality. Extensive feature extraction was applied to capture both the overall properties of the time series and the correlation between the different measurements in the times series. The feature selection method consists of a greedy hybrid filter-