Learning the impact of acute and chronic diseases on forecasting neonatal encephalopathy.

Journal: Computer methods and programs in biomedicine
Published Date: Sep 13, 2021

Abstract

OBJECTIVE: There is a wide range of risk factors predisposing to the onset of neonatal encephalopathy (NE), including maternal antepartum/intrapartum comorbidities or events. However, few studies have investigated the difference in the impact of acute and chronic diseases on forecasting NE, which could assist clinicians in choosing the best course of action to prevent NE or reduce its severity and complications. In this study, we aimed to engineer features based on acute and chronic diseases and assess the differences of the impact of acute and chronic diseases on NE prediction using machine learning models.

Authors

  • Eugene Jeong
    Ajou University School of Medicine, Department of Biomedical Informatics, Suwon, 16499, Republic of Korea.
  • Sarah Osmundson
    Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee.
  • Cheng Gao
    Dept. of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, USA.
  • Digna R Velez Edwards
    Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Obstetrics and Gynecology, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.
  • Bradley Malin
    Vanderbilt University Medical Center, Nashville, TN, United States.
  • You Chen
    Dept. of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, USA.

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