Machine learning for predicting elective fertility preservation outcomes
Machine learning for predicting elective fertility preservation outcomes
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Publisher
London: Nature Publishing Group UK
Journal title
Language
English
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Publisher
London: Nature Publishing Group UK
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Scope and Contents
Contents
This retrospective study applied machine-learning models to predict treatment outcomes of women undergoing elective fertility preservation. Two-hundred-fifty women who underwent elective fertility preservation at a tertiary center, 2019–2022 were included. Primary outcome was the number of metaphase II oocytes retrieved. Outcome class was based on...
Alternative Titles
Full title
Machine learning for predicting elective fertility preservation outcomes
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TN_cdi_doaj_primary_oai_doaj_org_article_191f07ed9e3c4b31bb57e48ad20dff8f
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_191f07ed9e3c4b31bb57e48ad20dff8f
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-024-60671-w
