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Machine learning prediction models for postpartum depression: A multicenter study in Japan.

Seiko MatsuoTakafumi UshidaRyo EmotoYoshinori MoriyamaYukako IitaniNoriyuki NakamuraKenji ImaiTomoko Nakano-KobayashiShigeru YoshidaMamoru YamashitaShigeyuki MatsuiHiroaki KajiyamaTomomi Kotani
Published in: The journal of obstetrics and gynaecology research (2022)
Our machine learning models did not achieve better predictive performance for PPD than conventional logistic regression models. However, we demonstrated the usefulness of the 2-week postpartum checkup for the identification of women at high risk of PPD.
Keyphrases
  • machine learning
  • depressive symptoms
  • polycystic ovary syndrome
  • randomized controlled trial
  • pregnant women
  • sleep quality
  • metabolic syndrome
  • skeletal muscle
  • insulin resistance