  We present a neural network framework for learning a survival model to
predict a time-to-event outcome while simultaneously learning a topic model
that reveals feature relationships. In particular, we model each subject as a
distribution over "topics", where a topic could, for instance, correspond to an
age group, a disorder, or a disease. The presence of a topic in a subject means
that specific clinical features are more likely to appear for the subject.
Topics encode information about related features and are learned in a
supervised manner to predict a time-to-event outcome. Our framework supports
combining many different topic and survival models; training the resulting
joint survival-topic model readily scales to large datasets using standard
neural net optimizers with minibatch gradient descent. For example, a special
case is to combine LDA with a Cox model, in which case a subject's distribution
over topics serves as the input feature vector to the Cox model. We explain how
to address practical implementation issues that arise when applying these
neural survival-supervised topic models to clinical data, including how to
visualize results to assist clinical interpretation. We study the effectiveness
of our proposed framework on seven clinical datasets on predicting time until
death as well as hospital ICU length of stay, where we find that neural
survival-supervised topic models achieve competitive accuracy with existing
approaches while yielding interpretable clinical topics that explain feature
relationships. Our code is available at:
https://github.com/georgehc/survival-topics
