  We develop an unsupervised probabilistic model for heterogeneous Electronic
Health Record (EHR) data. Utilizing a mixture model formulation, our approach
directly models sequences of arbitrary length, such as medications and
laboratory results. This allows for subgrouping and incorporation of the
dynamics underlying heterogeneous data types. The model consists of a layered
set of latent variables that encode underlying structure in the data. These
variables represent subject subgroups at the top layer, and unobserved states
for sequences in the second layer. We train this model on episodic data from
subjects receiving medical care in the Kaiser Permanente Northern California
integrated healthcare delivery system. The resulting properties of the trained
model generate novel insight from these complex and multifaceted data. In
addition, we show how the model can be used to analyze sequences that
contribute to assessment of mortality likelihood.
