A Bayesian network model for predicting pregnancy after in vitro fertilization

Authors:
G. Corani;C. Magli;A. Giusti;L. Gianaroli;L. M. Gambardella
Affiliations:
-;-;-;-;-
Venue:
Computers in Biology and Medicine
Year:
2013

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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.