Identifying Elective Induction of Labor among a Diverse Pregnant Population from Electronic Health Records within a Large Integrated Health Care System.

Journal: American journal of perinatology
PMID: 39209302

Abstract

OBJECTIVE:  Distinguishing between medically indicated induction of labor (iIOL) and elective induction of labor (eIOL) is a daunting process for researchers. We aimed to develop a Natural Language Processing (NLP) algorithm to identify eIOLs from electronic health records (EHRs) within a large integrated health care system.

Authors

  • Fagen Xie
    Kaiser Permanente Southern California, Pasadena, CA, USA.
  • Michael J Fassett
    Department of Obstetrics and Gynecology, Kaiser Permanente West Los Angeles Medical Center, Los Angeles, California.
  • Theresa M Im
    Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California.
  • Daniella Park
    Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California.
  • Vicki Y Chiu
    Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California.
  • Darios Getahun
    Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California.

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