Natural language processing of admission notes to predict severe maternal morbidity during the delivery encounter.

Journal: American journal of obstetrics and gynecology
Published Date: Apr 14, 2022

Abstract

BACKGROUND: Severe maternal morbidity and mortality remain public health priorities in the United States, given their high rates relative to other high-income countries and the notable racial and ethnic disparities that exist. In general, accurate risk stratification methods are needed to help patients, providers, hospitals, and health systems plan for and potentially avert adverse outcomes.

Authors

  • Mark A Clapp
    Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA, USA. mark.clapp@mgh.harvard.edu.
  • Ellen Kim
    Department of Radiation Oncology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
  • Kaitlyn E James
    Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA, USA.
  • Roy H Perlis
    Center for Quantitative Health, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
  • Anjali J Kaimal
    Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA, USA.
  • Thomas H McCoy
    Center for Quantitative Health, Department of Psychiatry and Department of Medicine, Massachusetts General Hospital, Boston, MA. Electronic address: thmccoy@partners.org.

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