Interpretable deep neural networks for advancing early neonatal birth weight prediction using multimodal maternal factors.

Journal: Journal of biomedical informatics
Published Date: Jun 1, 2025

Abstract

BACKGROUND: Neonatal low birth weight (LBW) is a significant predictor of increased morbidity and mortality among newborns. Predominantly, traditional prediction methods depend heavily on ultrasonography, which does not consider risk factors affecting birth weight (BW).

Authors

  • Muhammad Mursil
    Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Tarragona, 43007, Tarragona, Spain. Electronic address: muhammad.mursil@urv.cat.
  • Hatem A Rashwan
    Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Tarragona, 43007, Tarragona, Spain.
  • Adnan Khalid
    Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Tarragona, 43007, Tarragona, Spain.
  • Pere CavallĂ©-Busquets
    Unit of Obstetrics & Gynaecology, University Hospital Sant Joan, Reus, IISPV, CIBERObn ISCII, 43201, Tarragona, Spain.
  • Luis Santos-Calderon
    Faculty of Medicine and Health Sciences, IISPV, Universitat Rovira i Virgili, Reus, CIBERObn ISCIII, 43201, Tarragona, Spain.
  • Michelle M Murphy
    Faculty of Medicine and Health Sciences, IISPV, Universitat Rovira i Virgili, Reus, CIBERObn ISCIII, 43201, Tarragona, Spain.
  • Domenec Puig
    Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Tarragona, 43007, Tarragona, Spain.

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