Intrapartum electronic fetal heart rate monitoring to predict acidemia at birth with the use of deep learning.

Jennifer A McCoy¹, Lisa D Levine², Guangya Wan³, Corey Chivers⁴, Joseph Teel⁵, William G La Cava⁶

¹Maternal Fetal Medicine Research Program, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA. Electronic address: Jennifer.mccoy@pennmedicine.upenn.edu.

²Maternal Fetal Medicine Research Program, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

³School of Data Science, University of Virginia, Charlottesville, VA.

⁴Proscia Inc, Philadelphia, PA.

⁵Department of Family Medicine and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

⁶Computational Health Informatics Program, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA.

PubMed PMC DOI PDF

Abstract

BACKGROUND: Electronic fetal monitoring is used in most US hospital births but has significant limitations in achieving its intended goal of preventing intrapartum hypoxic-ischemic injury. Novel deep learning techniques can improve complex data processing and pattern recognition in medicine. OBJECTIVE: This study aimed to apply deep learning approaches to develop and validate a model to predict f…

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