Online Program Home
My Program

Abstract Details

Activity Number: 485 - Bayesian Latent Variable Methods for Life Sciences
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #330797 Presentation
Title: A Latent Bayesian Classification Model to Predict Kidney Obstruction Based on Renography and Expert Ratings
Author(s): Changgee Chang* and Jeong Hoon Jang and Amita Manatunga and Qi Long
Companies: University of Pennsylvania and Emory University and Emory University and University of Pennsylvania
Keywords: Bayesian latent classification; factor model

Kidney obstruction is a serious disease which can lead to loss of renal function when not treated in a timely manner. Diuresis renography is widely used to detect obstruction in kidney. However, the diagnosis largely relies on experts' experiences, and there is no gold standard statistical approach designed to analyze renogram curves and clinical variables associated with patients. In this work, we propose an integrative Bayesian approach that models the triplet jointly: renogram curves, clinical variables of patients, and experts' ratings, conditional on the latent kidney obstruction status. In particular, we adopt a nonparametric approach for modeling renogram curves in which the coefficients of the basis functions are parameterized using latent factors that are dependent on the latent disease status. We develop an MCMC training algorithm and an associated prediction algorithm for kidney obstruction that are computationally efficient. We demonstrate the superior performance of our proposed method in comparison with several naïve approaches via extensive simulations as well as analysis of real data collected from a kidney obstruction study.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program