Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting: An Application of Machine Learning Methods.

Journal: Medical decision making : an international journal of the Society for Medical Decision Making

Published Date: Aug 1, 2015

Abstract

BACKGROUND: Multiple embryo transfers in in vitro fertilization (IVF) treatment increase the number of successful pregnancies while elevating the risk of multiple gestations. IVF-associated multiple pregnancies exhibit significant financial, social, and medical implications. Clinicians need to decide the number of embryos to be transferred considering the tradeoff between successful outcomes and multiple pregnancies.

Authors

Asli Uyar

Department of Computer Engineering, Okan University, Tuzla Kampusu, Tuzla, Istanbul, Turkey (AU)
Ayse Bener

Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, Ontario, Canada (AB)
H Nadir Ciray

Division of Reproduction and Early Development, Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, Leeds, UK (HNC)

Keywords

Adult Algorithms Cohort Studies Decision Support Techniques Embryo Implantation Embryo Transfer Female Fertilization in Vitro Humans Machine Learning Outcome Assessment, Health Care Pregnancy Pregnancy, Multiple Retrospective Studies Sperm Injections, Intracytoplasmic Turkey

External Resources

View on PubMed Access via DOI PubMed (24842951)

Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting: An Application of Machine Learning Methods.

Abstract

Authors

Keywords

External Resources

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