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Developing and Validating a Machine Learning Approach for Prediction of Euploid Pregnancy Loss – Fertility and Sterility
Fertility and Sterility/LinkedIn
Jun 14, 2026, 13:08

Developing and Validating a Machine Learning Approach for Prediction of Euploid Pregnancy Loss – Fertility and Sterility

Fertility and Sterility shared a post on LinkedIn:

“Developing and validating a machine learning approach for prediction of euploid pregnancy loss in the Copenhagen pregnancy loss study

Full text.”

Title: Developing and validating a machine learning approach for prediction of euploid pregnancy loss in the Copenhagen pregnancy loss study

Authors: Karina Banasik, Inga Tummoszeit, Sofie Bliddal, Laura E. Vexø, Emilie P. Madsen, Tanja S. Hartwig, Lene Werge, Morten G. Johnsen, Jennifer R. Gruhn, Andrew C. Chan, Ellen C. Løkkegaard, Louise Ambye, Finn S. Jørgensen, The COPL Consortium, Eva R. Hoffmann, David Westergaard, Henriette S. Nielsen

Developing and Validating a Machine Learning Approach for Prediction of Euploid Pregnancy Loss – Fertility and Sterility

Stay updated on all scientific advances in the field of fertility with Fertility News.

Andrew C. Chan Copenhagen pregnancy loss study David Westergaard Ellen C. Løkkegaard Emilie P. Madsen euploid pregnancy loss Eva R. Hoffmann Fertility Fertility and Sterility Fertility News Finn S. Jørgensen Henriette S. Nielsen Inga Tummoszeit Jennifer R. Gruhn Karina Banasik Laura E. Vexø Lene Werge Louise Ambye Machine Learning Morten G. Johnsen OBGYN reproductive health Reproductive Medicine Sofie Bliddal Tanja S. Hartwig The COPL Consortium
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