  There is a great need for technologies that can predict the mortality of
patients in intensive care units with both high accuracy and accountability. We
present joint end-to-end neural network architectures that combine long
short-term memory (LSTM) and a latent topic model to simultaneously train a
classifier for mortality prediction and learn latent topics indicative of
mortality from textual clinical notes. For topic interpretability, the topic
modeling layer has been carefully designed as a single-layer network with
constraints inspired by LDA. Experiments on the MIMIC-III dataset show that our
models significantly outperform prior models that are based on LDA topics in
mortality prediction. However, we achieve limited success with our method for
interpreting topics from the trained models by looking at the neural network
weights.
