  In the dynamic hospital setting, decision support can be a valuable tool for
improving patient outcomes. Data-driven inference of future outcomes is
challenging in this dynamic setting, where long sequences such as laboratory
tests and medications are updated frequently. This is due in part to
heterogeneity of data types and mixed-sequence types contained in variable
length sequences. In this work we design a probabilistic unsupervised model for
multiple arbitrary-length sequences contained in hospitalization Electronic
Health Record (EHR) data. The model uses a latent variable structure and
captures complex relationships between medications, diagnoses, laboratory
tests, neurological assessments, and medications. It can be trained on original
data, without requiring any lossy transformations or time binning. Inference
algorithms are derived that use partial data to infer properties of the
complete sequences, including their length and presence of specific values. We
train this model on data from subjects receiving medical care in the Kaiser
Permanente Northern California integrated healthcare delivery system. The
results are evaluated against held-out data for predicting the length of
sequences and presence of Intensive Care Unit (ICU) in hospitalization bed
sequences. Our method outperforms a baseline approach, showing that in these
experiments the trained model captures information in the sequences that is
informative of their future values.
