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There is a great need for technologies that can predict the mortality ofpatients in intensive care units with both high accuracy and accountability. Wepresent joint end-to-end neural network architectures that combine longshort-term memory (LSTM) and a latent topic model to simultaneously train aclassifier for mortality prediction and learn latent topics indicative ofmortality from textual clinical notes. For topic interpretability, the topicmodeling layer has been carefully designed as a single-layer network withconstraints inspired by LDA. Experiments on the MIMIC-III dataset show that ourmodels significantly outperform prior models that are based on LDA topics inmortality prediction. However, we achieve limited success with our method forinterpreting topics from the trained models by looking at the neural networkweights.

Combining LSTM and Latent Topic Modeling for Mortality Prediction