Identifying Patterns of Depression Comorbidities Using Association Rule Learning: Insights from Maryland Medicaid Data
10/31/2025
Hilltop Principal Data Scientist Fei Han, PhD, Principal Policy Analyst Christine Gill, PhD, and Senior Policy Specialist Elizabeth Blake coauthored this article published in Healthcare Informatics Research with UMBC Associate Professor Ian Stockwell, PhD. The study aimed to identify association rules in patients with multiple chronic conditions, with a focus on patterns involving depression, a highly prevalent psychiatric disorder and a significant risk factor for suicide. Understanding comorbidity patterns in patients with depression is critical for targeting screening efforts, enabling early diagnosis, and improving chronic disease management.