Literature DB >> 24209924

A Bayesian network model for predicting pregnancy after in vitro fertilization.

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

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.
© 2013 Elsevier Ltd. All rights reserved.

Entities:  

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

Mesh：

Year:  2013        PMID： 24209924     DOI： 10.1016/j.compbiomed.2013.07.035

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN： 0010-4825            Impact factor:   4.589


  8 in total

Review 1.  Are computational applications the "crystal ball" in the IVF laboratory? The evolution from mathematics to artificial intelligence.

Authors:  Mara Simopoulou; Konstantinos Sfakianoudis; Evangelos Maziotis; Nikolaos Antoniou; Anna Rapani; George Anifandis; Panagiotis Bakas; Stamatis Bolaris; Agni Pantou; Konstantinos Pantos; Michael Koutsilieris
Journal:  J Assist Reprod Genet       Date:  2018-07-27       Impact factor: 3.412

Review 2.  Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data.

Authors:  Eleonora Inácio Fernandez; André Satoshi Ferreira; Matheus Henrique Miquelão Cecílio; Dóris Spinosa Chéles; Rebeca Colauto Milanezi de Souza; Marcelo Fábio Gouveia Nogueira; José Celso Rocha
Journal:  J Assist Reprod Genet       Date:  2020-07-11       Impact factor: 3.412

3.  Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991-2020).

Authors:  Roohallah Alizadehsani; Mohamad Roshanzamir; Sadiq Hussain; Abbas Khosravi; Afsaneh Koohestani; Mohammad Hossein Zangooei; Moloud Abdar; Adham Beykikhoshk; Afshin Shoeibi; Assef Zare; Maryam Panahiazar; Saeid Nahavandi; Dipti Srinivasan; Amir F Atiya; U Rajendra Acharya
Journal:  Ann Oper Res       Date:  2021-03-21       Impact factor: 4.820

4.  Bayesian-Based Decision Support System for Assessing the Needs for Orthodontic Treatment.

Authors:  Bhornsawan Thanathornwong
Journal:  Healthc Inform Res       Date:  2018-01-31

Review 5.  A Review of Machine Learning Approaches in Assisted Reproductive Technologies.

Authors:  Behnaz Raef; Reza Ferdousi
Journal:  Acta Inform Med       Date:  2019-09

6.  Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study.

Authors:  Qingsong Xi; Qiyu Yang; Meng Wang; Bo Huang; Bo Zhang; Zhou Li; Shuai Liu; Liu Yang; Lixia Zhu; Lei Jin
Journal:  Reprod Biol Endocrinol       Date:  2021-04-05       Impact factor: 5.211

7.  Pseudo contrastive labeling for predicting IVF embryo developmental potential.

Authors:  I Erlich; A Ben-Meir; I Har-Vardi; J Grifo; F Wang; C Mccaffrey; D McCulloh; Y Or; L Wolf
Journal:  Sci Rep       Date:  2022-02-15       Impact factor: 4.379

8.  Big data analytics for preventive medicine.

Authors:  Muhammad Imran Razzak; Muhammad Imran; Guandong Xu
Journal:  Neural Comput Appl       Date:  2019-03-16       Impact factor: 5.102
  8 in total

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