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Clinical measurements that can be represented as time series constitute animportant fraction of the electronic health records and are often bothuncertain and incomplete. Recurrent neural networks are a special class ofneural networks that are particularly suitable to process time series data but,in their original formulation, cannot explicitly deal with missing data. Inthis paper, we explore imputation strategies for handling missing values inclassifiers based on recurrent neural network (RNN) and apply a recentlyproposed recurrent architecture, the Gated Recurrent Unit with Decay,specifically designed to handle missing data. We focus on the problem ofdetecting surgical site infection in patients by analyzing time series of theirblood sample measurements and we compare the results obtained with differentRNN-based classifiers.

Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks