  Multiple cause-of-death data provides a valuable source of information that
can be used to enhance health standards by predicting health related
trajectories in societies with large populations. These data are often
available in large quantities across U.S. states and require Big Data
techniques to uncover complex hidden patterns. We design two different classes
of models suitable for large-scale analysis of mortality data, a Hadoop-based
ensemble of random forests trained over N-grams, and the DeepDeath, a deep
classifier based on the recurrent neural network (RNN). We apply both classes
to the mortality data provided by the National Center for Health Statistics and
show that while both perform significantly better than the random classifier,
the deep model that utilizes long short-term memory networks (LSTMs), surpasses
the N-gram based models and is capable of learning the temporal aspect of the
data without a need for building ad-hoc, expert-driven features.
