Variable Selection in Covariate Dependent Random Partition Models: an Application to Urinary Tract Infection

TL;DR

This paper introduces a Bayesian nonparametric model for clustering patients based on urinary tract infection indicators and symptoms, enabling identification of key symptoms associated with UTI for early diagnosis.

Contribution

It develops a covariate-dependent Dirichlet Process model with spike and slab priors for symptom selection, advancing personalized clustering in medical diagnostics.

Findings

Effective clustering of patients based on symptoms and WBC levels

Identification of key symptoms associated with UTI

Improved understanding of symptom patterns in UTI patients

Abstract

Lower urinary tract symptoms (LUTS) can indicate the presence of urinary tract infection (UTI), a condition that if it becomes chronic requires expensive and time consuming care as well as leading to reduced quality of life. Detecting the presence and gravity of an infection from the earliest symptoms is then highly valuable. Typically, white blood cell count (WBC) measured in a sample of urine is used to assess UTI. We consider clinical data from 1341 patients at their first visit in which UTI (i.e. WBC$\geq 1$) is diagnosed. In addition, for each patient, a clinical profile of 34 symptoms was recorded. In this paper we propose a Bayesian nonparametric regression model based on the Dirichlet Process (DP) prior aimed at providing the clinicians with a meaningful clustering of the patients based on both the WBC (response variable) and possible patterns within the symptoms profiles (covariates). This is achieved by assuming a probability model for the symptoms as well as for the response variable. To identify the symptoms most associated to UTI, we specify a spike and slab base measure for the regression coefficients: this induces dependence of symptoms selection on cluster assignment. Posterior inference is performed through Markov Chain Monte Carlo methods.