In the hospital setting, a small percentage of recurrent frequent patients contribute to a disproportional amount of healthcare resource utilization. Moreover, in many of these cases, patient outcomes can be greatly improved by reducing re-occurring visits, especially when they are associated with substance abuse, mental health, and medical factors that could be improved by social-behavioral interventions, outpatient or preventative care. Additionally, health care costs can be reduced significantly with fewer preventable recurrent visits. To address this, we developed a novel, interpretable framework that both identifies recurrent patients with high utilization and determines which comorbidities contribute most to their recurrent visits. Specifically, we present a novel algorithm, called the minimum similarity association rules (MSAR), which balances the confidence-support trade-off, to determine the conditions most associated with re-occurring Emergency department and inpatient visits. We validate MSAR on a large Electronic Health Record dataset, demonstrating the effectiveness and consistency in ability to find low-support comorbidities with high likelihood of being associated with recurrent visits, which is challenging for other algorithms such as XGBoost. Clinical relevance- In the era of value-based care and population health management, the proposal could be used for decision making to help reduce future recurrent admissions, improve patient outcomes and reduce the cost of healthcare.