Interpretable deep neural networks for advancing early neonatal birth weight prediction using multimodal maternal factors
Year: 2025
Type: article
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). 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.
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,410
Funders: Institut d'Investigació Sanitària Pere Virgili, European Commission, Generalitat de Catalunya, Departament d'Innovació, Universitats i Empresa, Generalitat de Catalunya, Centro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y Nutrición, Italfarmaco, Horizon 2020 Framework Programme, Agència de Gestió d'Ajuts Universitaris i de Recerca, Instituto de Salud Carlos III, Horizon 2020, European Regional Development Fund, Agencia Estatal de Investigación
Awards: The second coordination and support action for the JPI Healthy Diet for a Healthy Life, G1879907128, G2649958294, G46484185, Un estudio del vínculo entre el metabolismo monocarbonado in útero y desarrollo y salud en el niño de 7,5-8 años, G7501389082, G7701066576, G7779455848, G7844125592, Alteraciones genéticas y metabólicas en el metabolismo monocarbonado paterno y desarrollo de complicaciones del embarazo de origen placentario, Interacción entre la regulación prenatal nutriente-gen, el tabaquismo parental y los procesos epigenéticos que determinan el patrón de desarrollo y la salud infantil. The Reus Tarragona Birth Cohort.
OpenAlex ID: W4410118486