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Enhancing predictive models for egg donation: time to blastocyst hatching and machine learning insights.

Jorge TenLeyre HerreroÁngel LinaresElisa ÁlvarezJosé Antonio OrtizAndrea BernabeuRafael Bernabeu
Published in: Reproductive biology and endocrinology : RB&E (2024)
This study highlights Random Forest and AdaBoost as the most effective machine learning models in our Known Implantation and Live Birth Database from our egg donation program. Notably, time to blastocyst hatching out of the zona pellucida emerged as a highly reliable parameter significantly influencing our implantation machine learning predictive models. Processes involving syngamy, genomic imprinting during embryo cleavage, and embryo compaction are also influential and could be crucial for implantation and live birth outcomes.
Keyphrases
  • machine learning
  • artificial intelligence
  • big data
  • pregnancy outcomes
  • deep learning
  • climate change
  • quality improvement
  • emergency department
  • pregnant women
  • dna methylation
  • skeletal muscle
  • gene expression