A Bayesian network model for predicting pregnancy after in vitro fertilization

G Corani; C Magli; A Giusti; L Gianaroli; L M Gambardella

doi:10.1016/j.compbiomed.2013.07.035

A Bayesian network model for predicting pregnancy after in vitro fertilization

Comput Biol Med. 2013 Nov;43(11):1783-92. doi: 10.1016/j.compbiomed.2013.07.035. Epub 2013 Aug 14.

Authors

G Corani¹, C Magli, A Giusti, L Gianaroli, L M Gambardella

Affiliation

¹ Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Manno, Switzerland. Electronic address: giorgio@idsia.ch.

PMID: 24209924
DOI: 10.1016/j.compbiomed.2013.07.035

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.

Keywords: Bayesian networks; Classification; EM algorithm; In vitro fertilization (IVF); MAP estimation.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Algorithms
Area Under Curve
Bayes Theorem*
Computer Simulation
Embryo Transfer / statistics & numerical data*
Embryo, Mammalian
Female
Fertilization in Vitro / statistics & numerical data*
Humans
Pregnancy / statistics & numerical data