Co-occurrence of medical conditions: Exposing patterns through probabilistic topic modeling of SNOMED codes

TL;DR

This study uses probabilistic topic modeling on SNOMED codes from EHRs of over 13,000 kidney disease patients to uncover meaningful patterns of co-occurring medical conditions, revealing both known and novel associations.

Contribution

It introduces a novel application of topic modeling directly on medical codes to identify and validate co-occurrence patterns in large patient datasets.

Findings

Topics are characterized by a few highly probable codes, indicating tightness.

High inter-topic distances show the topics are distinct.

Most conditions within topics co-occur in medical literature, with some novel associations uncovered.

Abstract

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.