Rule-based natural language processing to examine variation in worsening heart failure hospitalizations by age, sex, race and ethnicity, and left ventricular ejection fraction.

Journal: American heart journal
PMID: 39251103

Abstract

BACKGROUND: Prior studies characterizing worsening heart failure events (WHFE) have been limited in using structured healthcare data from hospitalizations, and with little exploration of sociodemographic variation. The current study examined the impact of incorporating unstructured data to identify WHFE, describing age-, sex-, race and ethnicity-, and left ventricular ejection fraction (LVEF)-specific rates.

Authors

  • Matthew T Mefford
    From the Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA. Electronic address: matthew.t.mefford@kp.org.
  • Andrew P Ambrosy
    Department of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, California.
  • Rong Wei
    From the Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA.
  • Chengyi Zheng
    Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California. Electronic address: Chengyi.X.Zheng@kp.org.
  • Rishi V Parikh
    Division of Research, Kaiser Permanente Northern California, Oakland.
  • Teresa N Harrison
    From the Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA.
  • Ming-Sum Lee
    Department of Cardiology, Los Angeles Medical Center, Kaiser Permanente Southern California, Pasadena, CA, United States.
  • Alan S Go
    Division of Research, Kaiser Permanente Northern California, Oakland, California.
  • Kristi Reynolds
    From the Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA; Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA.

Keywords

External Resources

Popular Topics
Recent Journals