Learning Deep Representations from Clinical Data for Chronic Kidney Disease
This work addresses the challenge of handling noisy and irregular clinical data for CKD patients, but it appears incremental as it builds on an existing state-of-the-art method.
The paper tackled the problem of learning latent representations from irregularly sampled clinical data for Chronic Kidney Disease (CKD) patients, showing that addressing a key issue in recurrent neural network models leads to significant improvements in the learnt representations.
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.