Using Unlabeled Information of Embryo Siblings from the Same Cohort Cycle to Enhance In Vitro Fertilization Implantation Prediction (Adv. Sci. 27/2023)
Machine Learning In article number 2207711 Assaf Zaritsky and colleagues enhance the performance of machine learning models to predict embryo implantation potential by using embryo cohort‐derived information. Using information encapsulated by the correlated “sibling” cohort embryos reduces the inher...
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Published in | Advanced science Vol. 10; no. 27 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Weinheim
John Wiley & Sons, Inc
01.09.2023
John Wiley and Sons Inc |
Subjects | |
Online Access | Get full text |
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Summary: | Machine Learning
In article number
2207711
Assaf Zaritsky and colleagues enhance the performance of machine learning models to predict embryo implantation potential by using embryo cohort‐derived information. Using information encapsulated by the correlated “sibling” cohort embryos reduces the inherent noise of the individual transferred embryo associated with its implantation uncertainty. |
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ISSN: | 2198-3844 2198-3844 |
DOI: | 10.1002/advs.202370183 |