A Bayesian nonparametric model for white blood cells in patients with lower urinary tract symptoms

Abstract

Lower Urinary Tract Symptoms (LUTS) affect a significant proportion of the population and often lead to a reduced quality of life. LUTS overlap across a wide variety of diseases, which makes the diagnostic process extremely complicated. In this work we focus on the relation between LUTS and Urinary Tract Infection (UTI). The latter is detected through the number of White Blood Cells (WBC) in a sample of urine: WBC$\geq1$ indicates UTI and high levels may indicate complications. The objective of this work is to provide the clinicians with a tool for supporting the diagnostic process, deepening the available knowledge about LUTS and UTI. We analyze data recording both LUTS profile and WBC count for each patient. We propose to model the WBC using a random partition model in which we specify a prior distribution over the partition of the patients which includes the clustering information contained in the LUTS profile. Then, within each cluster, the WBC counts are assumed to be generated by a zero-inflated Poisson distribution. The results of the predictive distribution allows to identify the symptoms configuration most associated with the presence of UTI as well as with severe infections.