  Identifying a patient's key problems over time is a common task for providers
at the point care, yet a complex and time-consuming activity given current
electric health records. To enable a problem-oriented summarizer to identify a
patient's comprehensive list of problems and their salience, we propose an
unsupervised phenotyping approach that jointly learns a large number of
phenotypes/problems across structured and unstructured data. To identify the
appropriate granularity of the learned phenotypes, the model is trained on a
target patient population of the same clinic. To enable the content
organization of a problem-oriented summarizer, the model identifies phenotype
relatedness as well. The model leverages a correlated-mixed membership approach
with variational inference applied to heterogenous clinical data. In this
paper, we focus our experiments on assessing the learned phenotypes and their
relatedness as learned from a specific patient population. We ground our
experiments in phenotyping patients from an HIV clinic in a large urban care
institution (n=7,523), where patients have voluminous, longitudinal
documentation, and where providers would benefit from summaries of these
patient's medical histories, whether about their HIV or any comorbidities. We
find that the learned phenotypes and their relatedness are clinically valid
when assessed qualitatively by clinical experts, and that the model surpasses
baseline in inferring phenotype-relatedness when comparing to existing
expert-curated condition groupings.
