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Across several medical fields, developing an approach for diseaseclassification is an important challenge. The usual procedure is to fit a modelfor the longitudinal response in the healthy population, a different model forthe longitudinal response in disease population, and then apply the Bayes'theorem to obtain disease probabilities given the responses. Unfortunately,when substantial heterogeneity exists within each population, this type ofBayes classification may perform poorly. In this paper, we develop a newapproach by fitting a Bayesian nonparametric model for the joint outcome ofdisease status and longitudinal response, and then use the clustering inducedby the Dirichlet process in our model to increase the flexibility of themethod, allowing for multiple subpopulations of healthy, diseased, and possiblymixed membership. In addition, we introduce an MCMC sampling scheme thatfacilitates the assessment of the inference and prediction capabilities of ourmodel. Finally, we demonstrate the method by predicting pregnancy outcomesusing longitudinal profiles on the $\beta$--HCG hormone levels in a sample ofChilean women being treated with assisted reproductive therapy.

A Bayesian Nonparametric Model for Predicting Pregnancy Outcomes Using Longitudinal Profiles