Autor segons l'article: Mursil, M; Rashwan, HA; Khalid, A; Cavallé-Busquets, P; Santos-Calderon, L; Murphy, MM; Puig, D
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / KHALID, ADNAN / Murphy, Michelle / Puig Valls, Domènec Savi
Paraules clau: Associatio; Birth weight prediction; Deep learning; Explainable ai; Fetal development; Machine; Machine learning; Maternal factor; Maternal factors; Maternal health; Random forest
Resum: 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). Objective: This study introduces a robust deep neural network for a clinical decision-support system designed to early predict neonatal BW, using data available during early pregnancy, with enhanced precision. This innovative system incorporates a comprehensive array of maternal factors, placing particular emphasis on nutritional elements alongside physiological and lifestyle variables. Methods: We employed and validated various traditional machine learning models as well as an interpretable deep learning model using the TabNet architecture, noted for its proficient handling of tabular data and high level of interpretability. The efficacy of these models was evaluated against extensive datasets that encompass a broad spectrum of maternal health indicators. Results: The TabNet model exhibited outstanding predictive capabilities, achieving an accuracy of 96% and an area under the curve (AUC) of 0.96. Significantly, maternal vitamin B12 and folate status emerged as pivotal predictors of BW, emphasizing the crucial role of nutritional factors in influencing neonatal health outcomes. Conclusions: Our results demonstrate the substantial benefits of integrating multimodal maternal factors into predictive models for neonatal BW, markedly enhancing the precision over traditional AI methods. The developed decision-support system not only has a possible application in prenatal care but also provides actionable insights that can be leveraged to mitigate the risks associated with LBW, thereby improving clinical decision-making processes and outcomes.
Àrees temàtiques: Ciência da computação; Ciências biológicas i; Computer science applications; Computer science, interdisciplinary applications; Engenharias iv; Ensino; Health informatics; Interdisciplinar; Mathematical & computational biology; Medical informatics; Saúde coletiva
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: domenec.puig@urv.cat; michelle.murphy@urv.cat; hatem.abdellatif@urv.cat; adnan.khalid@urv.cat
Data d'alta del registre: 2026-02-09
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.sciencedirect.com/science/article/pii/S153204642500067X?via%3Dihub
Referència a l'article segons font original: Journal Of Biomedical Informatics. 166 104838-
Referència de l'ítem segons les normes APA: Mursil, M; Rashwan, HA; Khalid, A; Cavallé-Busquets, P; Santos-Calderon, L; Murphy, MM; Puig, D (2025). Interpretable deep neural networks for advancing early neonatal birth weight prediction using multimodal maternal factors. Journal Of Biomedical Informatics, 166(), 104838-. DOI: 10.1016/j.jbi.2025.104838
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
DOI de l'article: 10.1016/j.jbi.2025.104838
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2025-06-01
Tipus de publicació: Journal Publications
Repositori URV