  Patients associated with multiple co-occurring health conditions often face
aggravated complications and less favorable outcomes. Co-occurring conditions
are especially prevalent among individuals suffering from kidney disease, an
increasingly widespread condition affecting 13% of the general population in
the US. This study aims to identify and characterize patterns of co-occurring
medical conditions in patients employing a probabilistic framework.
Specifically, we apply topic modeling in a non-traditional way to find
associations across SNOMEDCT codes assigned and recorded in the EHRs of>13,000
patients diagnosed with kidney disease. Unlike most prior work on topic
modeling, we apply the method to codes rather than to natural language.
Moreover, we quantitatively evaluate the topics, assessing their tightness and
distinctiveness, and also assess the medical validity of our results. Our
experiments show that each topic is succinctly characterized by a few highly
probable and unique disease codes, indicating that the topics are tight.
Furthermore, inter-topic distance between each pair of topics is typically
high, illustrating distinctiveness. Last, most coded conditions grouped
together within a topic, are indeed reported to co-occur in the medical
literature. Notably, our results uncover a few indirect associations among
conditions that have hitherto not been reported as correlated in the medical
literature.
