  We study the behavior of a Time-Aware Long Short-Term Memory Autoencoder, a
state-of-the-art method, in the context of learning latent representations from
irregularly sampled patient data. We identify a key issue in the way such
recurrent neural network models are being currently used and show that the
solution of the issue leads to significant improvements in the learnt
representations on both synthetic and real datasets. A detailed analysis of the
improved methodology for representing patients suffering from Chronic Kidney
Disease (CKD) using clinical data is provided. Experimental results show that
the proposed T-LSTM model is able to capture the long-term trends in the data,
while effectively handling the noise in the signal. Finally, we show that by
using the latent representations of the CKD patients obtained from the T-LSTM
autoencoder, one can identify unusual patient profiles from the target
population.
