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Prediction of fetal RR intervals from maternal factors using machine learning models.
Widatalla, Namareq; Alkhodari, Mohanad; Koide, Kunihiro; Yoshida, Chihiro; Kasahara, Yoshiyuki; Saito, Masatoshi; Kimura, Yoshitaka; Habib Khandoker, Ahsan.
Afiliación
  • Widatalla N; Khalifa University, Abu Dhabi, UAE. namareq.widatalla@gmail.com.
  • Alkhodari M; Next Generation Biological Information Technology, Tohoku University Graduate School of Biomedical Engineering, Sendai, Japan. namareq.widatalla@gmail.com.
  • Koide K; Department of Biomedical Engineering, Healthcare Engineering Innovation Center, Khalifa University, Abu Dhabi, UAE.
  • Yoshida C; Radcliffe Department of Medicine, Cardiovascular Clinical Research Facility, University of Oxford, Oxford, UK.
  • Kasahara Y; Department of Maternal and Fetal Therapeutics, Tohoku University Graduate School of Medicine, Sendai, Japan.
  • Saito M; Advanced Interdisciplinary Biomedical Engineering, Tohoku University Graduate School of Medicine, Sendai, Japan.
  • Kimura Y; Department of Maternal and Fetal Therapeutics, Tohoku University Graduate School of Medicine, Sendai, Japan.
  • Habib Khandoker A; Advanced Interdisciplinary Biomedical Engineering, Tohoku University Graduate School of Medicine, Sendai, Japan.
Sci Rep ; 13(1): 19765, 2023 11 13.
Article en En | MEDLINE | ID: mdl-37957257
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Monitoreo Fetal / Feto Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article
Texto completo
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XML
PubMed Links
Buscar en Google

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Monitoreo Fetal / Feto Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article