Mining Medication Use Patterns from Clinical Notes for Breast Cancer Patients Through a Two-Stage Topic Modeling Approach

Abstract

Obtaining medication use and response information is essential for both care providers and researchers to understand patients' medication use and long-term treatment patterns. While unstructured clinical notes contain such information, they have rarely been analyzed for this purpose on a large scale due to the demands of expensive manual reviews. Here, we aimed to extract and analyze medication use patterns from clinical notes for a population of breast cancer patients at an academic medical center using unsupervised topic modeling techniques. Notably, we proposed a two-stage modeling process that was built upon correlated topic modeling (CTM) and structural topic modeling (STM) to capture nuanced information about medication behavior, including drug-disease relationships as well as medication schedules. The STM-derived topics show longitudinal prevalence patterns that may reflect changing patient needs and behaviors after the diagnosis of a severe disease. The patterns also show promise as a predictor for medication-taking behavior.