title: A model-based approach to Bayesian classification with applications to predicting pregnancy outcomes from longitudinal beta-hCG profiles.
abstract: This paper discusses Bayesian statistical methods for the classification of observations into two or more groups based on hierarchical models for nonlinear longitudinal profiles. Parameter estimation for a discriminant model that classifies individuals into distinct predefined groups or populations uses appropriate posterior simulation schemes. The methods are illustrated with data from a study involving 173 pregnant women. The main objective in this study is to predict normal versus abnormal pregnancy outcomes from beta human chorionic gonadotropin data available at early stages of pregnancy.
authors.affiliation: Departamento de Estadística, Facultad de Matemáticas, Pontificia Universidad Católica de Chile, Casilla 306, Correo 22, Santiago, Chile. rolando@mat.puc.cl
authors.forename: Rolando
authors.initials: R
authors.lastname: de la Cruz-Mesía
authors.forename: Fernando A
authors.initials: FA
authors.lastname: Quintana
mesh_terms: D001499:Bayes Theorem; D006063:Chorionic Gonadotropin / Q000097:blood*; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D011247:Pregnancy / Q000097:blood*; D011256:Pregnancy Outcome
chemical_list: D006063:Chorionic Gonadotropin