A data-driven health index for neonatal morbidities

Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep neural network model that simultaneously predicts...

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Bibliographic Details
Published in:iScience 2022-04, Vol.25 (4), p.104143-104143, Article 104143
Main Authors: De Francesco, Davide, Blumenfeld, Yair J., Marić, Ivana, Mayo, Jonathan A., Chang, Alan L., Fallahzadeh, Ramin, Phongpreecha, Thanaphong, Butwick, Alex J., Xenochristou, Maria, Phibbs, Ciaran S., Bidoki, Neda H., Becker, Martin, Culos, Anthony, Espinosa, Camilo, Liu, Qun, Sylvester, Karl G., Gaudilliere, Brice, Angst, Martin S., Stevenson, David K., Shaw, Gary M., Aghaeepour, Nima
Format: Article
Language:English
Subjects:
Biological sciences
Cell biology
Molecular biology
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Summary:Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep neural network model that simultaneously predicts twelve neonatal morbidities, as the basis for a new data-driven approach to define prematurity. Maternal demographics, medical history, obstetrical complications, and prenatal fetal findings were obtained from linked birth certificates and maternal/infant hospitalization records for 11,594,786 livebirths in California from 1991 to 2012. Overall, our model outperformed traditional models to assess prematurity which are based on GA and/or birthweight (area under the precision-recall curve was 0.326 for our model, 0.229 for GA, and 0.156 for small for GA). These findings highlight the potential of using machine learning techniques to predict multiple prematurity phenotypes and inform clinical decisions to prevent, diagnose and treat neonatal morbidities. [Display omitted] •Traditional definitions of prematurity based on gestational age need to be updated•Deep learning of maternal clinical data improves predictions of neonatal morbidity•Data-driven model leverages birthweight, type of delivery and maternal race•Accurate risk prediction can inform clinical decisions Biological sciences; Cell biology; Molecular biology
ISSN:2589-0042
2589-0042
DOI:10.1016/j.isci.2022.104143