Applying natural language processing to identify emergency department and observation encounters for worsening heart failure.

Journal: ESC heart failure
PMID: 38741373

Abstract

AIMS: Worsening heart failure (WHF) events occurring in non-inpatient settings are becoming increasingly recognized, with implications for prognostication. We evaluate the performance of a natural language processing (NLP)-based approach compared with traditional diagnostic coding for non-inpatient clinical encounters and left ventricular ejection fraction (LVEF).

Authors

  • Steven A Hamilton
    Department of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, CA, USA.
  • Andrew P Ambrosy
    Department of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, California.
  • Rishi V Parikh
    Division of Research, Kaiser Permanente Northern California, Oakland.
  • Thida C Tan
    Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
  • Jesse K Fitzpatrick
    Department of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, California.
  • Harshith R Avula
    Department of Cardiology, Kaiser Permanente Walnut Creek Medical Center, Walnut Creek, California.
  • Alexander T Sandhu
    Division of Cardiovascular Medicine, Department of Medicine, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA.
  • Ivy A Ku
    Department of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, CA, USA.
  • Alan S Go
    Division of Research, Kaiser Permanente Northern California, Oakland, California.
  • Dana Sax
    Department of Emergency Medicine, Kaiser Permanente Oakland Medical Center, Oakland, CA, USA.
  • Ankeet S Bhatt
    Department of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, CA, USA.

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