title: A Bayesian network model for predicting pregnancy after in vitro fertilization.
abstract: We present a Bayesian network model for predicting the outcome of in vitro fertilization (IVF). The problem is characterized by a particular missingness process; we propose a simple but effective averaging approach which improves parameter estimates compared to the traditional MAP estimation. We present results with generated data and the analysis of a real data set. Moreover, we assess by means of a simulation study the effectiveness of the model in supporting the selection of the embryos to be transferred.
authors.affiliation: Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Manno, Switzerland. Electronic address: giorgio@idsia.ch.
authors.forename: G
authors.initials: G
authors.lastname: Corani
authors.forename: C
authors.initials: C
authors.lastname: Magli
authors.forename: A
authors.initials: A
authors.lastname: Giusti
authors.forename: L
authors.initials: L
authors.lastname: Gianaroli
authors.forename: L M
authors.initials: LM
authors.lastname: Gambardella
mesh_terms: D000328:Adult; D000465:Algorithms; D019540:Area Under Curve; D001499:Bayes Theorem; D003198:Computer Simulation; D004624:Embryo Transfer / Q000706:statistics & numerical data*; D004622:Embryo, Mammalian; D005260:Female; D005307:Fertilization in Vitro / Q000706:statistics & numerical data*; D006801:Humans; D011247:Pregnancy / Q000706:statistics & numerical data
keywords: Bayesian networks; Classification; EM algorithm; In vitro fertilization (IVF); MAP estimation