Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record.

Journal: ESC heart failure
PMID: 38873749

Abstract

AIMS: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes. However, most HF patients are not hospitalized, and manual review of big electronic health record (EHR) data is not practical. The US Department of Veterans Affairs (VA) has the largest integrated healthcare system in the nation, and an estimated 1.5 million patients have ICD codes for HF (HF ICD-code universe) in their VA EHR. The objective of our study was to develop artificial intelligence (AI) models to phenotype HF in these patients.

Authors

  • Yijun Shao
    Veterans Affairs Medical Center, Washington, DC; George Washington University, Washington, DC.
  • Sijian Zhang
    Veterans Affairs Medical Center, Washington, DC.
  • Venkatesh K Raman
    Veterans Affairs Medical Center, Washington, DC; Georgetown University, Washington, DC.
  • Samir S Patel
    Veterans Affairs Medical Center, Washington, DC; George Washington University, Washington, DC.
  • Yan Cheng
    The First Clinical Medical College of Shaanxi University of Chinese Medicine, Xianyang, China.
  • Anshul Parulkar
    Veterans Affairs Medical Center, Providence, RI, USA.
  • Phillip H Lam
    Georgetown University, Washington, DC; MedStar Washington Hospital Center, Washington, DC.
  • Hans Moore
    Veterans Affairs Medical Center, Washington, DC; George Washington University, Washington, DC; Georgetown University, Washington, DC; Uniformed Services University, Washington, DC.
  • Helen M Sheriff
    Veterans Affairs Medical Center, Washington, DC; George Washington University, Washington, DC.
  • Gregg C Fonarow
    Ahmanson-UCLA Cardiomyopathy Center, Division of Cardiology, Ronald Reagan-UCLA Medical Center, Los Angeles, California.
  • Paul A Heidenreich
    Veterans Administration Palo Alto Health Care System, Palo Alto, California.
  • Wen-Chih Wu
    Veterans Affairs Medical Center, Providence, RI; Brown University, Providence, RI.
  • Ali Ahmed
    Veterans Affairs Medical Center, Washington, DC; George Washington University, Washington, DC; Georgetown University, Washington, DC. Electronic address: ali.ahmed@va.gov.
  • Qing Zeng-Treitler
    Veterans Affairs Medical Center, Washington, DC; George Washington University, Washington, DC.

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