Personalized Predictions for Unplanned Urinary Tract Infection Hospitalizations with Hierarchical Clustering

Urinary Tract Infection (UTI) is the one of the most frequent and preventable healthcare-associated infections in the US and an important cause of morbidity and excess healthcare costs. This study aims to predict the 30-day risk of a beneficiary for unplanned hospitalization for UTI. Using 2008–12 Medicare fee-for-service claims and several public sources, we extracted 784 features, including patient demographics, clinical conditions, healthcare utilization, provider quality metrics, and community safety indicators. To address the challenge of high heterogeneity and imbalance in data, we propose a hierarchical clustering approach that leverages existing knowledge and data-driven algorithms to partition the population into groups of similar risk, followed by building a LASSO-Logistic Regression (LLR) model for each group. Our prediction models are trained on 237,675 2011 Medicare beneficiaries and tested on 230,042 2012 Medicare beneficiaries. We compare the clustering-based approach to a baseline LLR model using five performance metrics, including the area under the curve (AUC), the True Positive Rate (TPR), and the False Positive Rate (FPR). Results show that the hierarchical clustering approach achieves more accurate and precise predictions (AUC 0.72) than the benchmark model and offers more granular feature importance insights for each patient group.