Deep learning predicts extreme preterm birth from electronic health records.

Journal: Journal of biomedical informatics
PMID: 31678588

Abstract

OBJECTIVE: Models for predicting preterm birth generally have focused on very preterm (28-32 weeks) and moderate to late preterm (32-37 weeks) settings. However, extreme preterm birth (EPB), before the 28th week of gestational age, accounts for the majority of newborn deaths. We investigated the extent to which deep learning models that consider temporal relations documented in electronic health records (EHRs) can predict EPB.

Authors

  • Cheng Gao
    Dept. of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, USA.
  • Sarah Osmundson
    Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee.
  • Digna R Velez Edwards
    Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Obstetrics and Gynecology, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Gretchen Purcell Jackson
    Vanderbilt University Medical Center, Nashville, Tennessee.
  • Bradley A Malin
    Vanderbilt University, Nashville, TN.
  • You Chen
    Dept. of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, USA.

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