Artificial Intelligence Approaches for Phenotyping Heart Failure in U.S. Veterans Health Administration Electronic Health Record

作者：Yijun Shao, Sijian Zhang, Venkatesh K. Raman, Samir S. Patel, Yan Cheng, Anshul Parulkar, Phillip H. Lam, H. J. Moore, Helen Sheriff, Gregg C. Fonarow, Paul A. Heidenreich, Wen‐Chih Wu, Ali Ahmed, Qing Zeng‐Treitler · 发表于：ESC Heart Failure · 年份：2024 · DOI：10.1002/ehf2.14787 · 被引用次数：14 · 研究领域：Machine Learning in Healthcare、Heart Failure Treatment and Management、Electronic Health Records Systems

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. METHODS AND RESULTS: The model development cohort (n = 20 000: training, 16 000; validation 2000; testing, 2000) included 10 000 patients with HF and 10 000 without HF who were matched by age, sex, race, inpatient/outpatient status, hospital, and encounter date (within 60 days). HF status was ascertained by manual chart reviews in VA's External Peer Review Program for HF (EPRP-HF) and non-HF status was ascertained by the absence of ICD codes for HF in VA EHR. Two clinicians annotated 1000 random snippets with HF-related keywords and labelled 436 as HF, which was then used to train and test a natural language processing (NLP) model to classify HF (positive predictive value or PPV, 0.81; sensitivity, 0.77). A machine learning (ML) model using linear support vector machine architecture was trained ...