Authors: Davide De Francesco, Yair J. Blumenfeld, Ivana Marić, Jonathan A. Mayo, Alan L. Chang, Ramin Fallahzadeh, Thanaphong Phongpreecha, Alex J. Butwick, Maria Xenochristou, Ciaran S. Phibbs, Neda H. Bidoki, Martin Becker, Anthony Culos, Camilo Espinosa, Qun Liu, Brice Gaudilliere, Martin S. Angst, David K. Stevenson, Gary M. Shaw, Nima Aghaeepour

Summary: While 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.

Download the R code here: R code

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