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Fitting the data from embryo implantation prediction: Learning from label proportions.

Authors
  • Hernández-González, Jerónimo1
  • Inza, Iñaki1
  • Crisol-Ortíz, Lorena2
  • Guembe, María A2
  • Iñarra, María J2
  • Lozano, Jose A1, 3
  • 1 1 Intelligent Systems Group, University of the Basque Country UPV/EHU, Spain. , (Spain)
  • 2 2 Unit of Assisted Reproduction, Osakidetza - Basque Public Health Service, Spain. , (Spain)
  • 3 3 Basque Center for Applied Mathematics BCAM, Spain. , (Spain)
Type
Published Article
Journal
Statistical Methods in Medical Research
Publisher
SAGE Publications
Publication Date
Apr 01, 2018
Volume
27
Issue
4
Pages
1056–1066
Identifiers
DOI: 10.1177/0962280216651098
PMID: 27242336
Source
Medline
Keywords
License
Unknown

Abstract

Machine learning techniques have been previously used to assist clinicians to select embryos for human-assisted reproduction. This work aims to show how an appropriate modeling of the problem can contribute to improve machine learning techniques for embryo selection. In this study, a dataset of 330 consecutive cycles (and associated embryos) carried out by the Unit of Assisted Reproduction of the Hospital Donostia (Spain) throughout 18 months has been analyzed. The problem of the embryo selection has been modeled by a novel weakly supervised paradigm, learning from label proportions, which considers all the available data, including embryos whose fate cannot be certainly established. Furthermore, all the collected features, describing cycles and embryos, have been considered in a multi-variate data analysis. Our integral solution has been successfully tested. Experimental results show that the proposed technique consistently outperforms an equivalent approach based on standard supervised classification. Embryos in this study were selected for transference according to the criteria of the Spanish Association for Reproduction Biology Studies. Obtained classification models outperform these criteria, specifically reordering medium-quality embryos.

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