@Article{info:doi/10.2196/64113,
author="Sumsion, Daniel
and Davis, Elijah
and Fernandes, Marta
and Wei, Ruoqi
and Milde, Rebecca
and Veltink, Jet Malou
and Kong, Wan-Yee
and Xiong, Yiwen
and Rao, Samvrit
and Westover, Tara
and Petersen, Lydia
and Turley, Niels
and Singh, Arjun
and Buss, Stephanie
and Mukerji, Shibani
and Zafar, Sahar
and Das, Sudeshna
and Junior, Valdery Moura
and Ghanta, Manohar
and Gupta, Aditya
and Kim, Jennifer
and Stone, Katie
and Mignot, Emmanuel
and Hwang, Dennis
and Trotti, Lynn Marie
and Clifford, Gari D
and Katwa, Umakanth
and Thomas, Robert
and Westover, M Brandon
and Sun, Haoqi",
title="Identification of Patients With Congestive Heart Failure From the Electronic Health Records of Two Hospitals: Retrospective Study",
journal="JMIR Med Inform",
year="2025",
month="Apr",
day="10",
volume="13",
pages="e64113",
keywords="electronic health record; machine learning; artificial intelligence; phenotype; congestive heart failure; medication; claims database; International Classification of Diseases; effectiveness; natural language processing; model performance; logistic regression; validity",
abstract="Background: Congestive heart failure (CHF) is a common cause of hospital admissions. Medical records contain valuable information about CHF, but manual chart review is time-consuming. Claims databases (using International Classification of Diseases [ICD] codes) provide a scalable alternative but are less accurate. Automated analysis of medical records through natural language processing (NLP) enables more efficient adjudication but has not yet been validated across multiple sites. Objective: We seek to accurately classify the diagnosis of CHF based on structured and unstructured data from each patient, including medications, ICD codes, and information extracted through NLP of notes left by providers, by comparing the effectiveness of several machine learning models. Methods: We developed an NLP model to identify CHF from medical records using electronic health records (EHRs) from two hospitals (Mass General Hospital and Beth Israel Deaconess Medical Center; from 2010 to 2023), with 2800 clinical visit notes from 1821 patients. We trained and compared the performance of logistic regression, random forests, and RoBERTa models. We measured model performance using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). These models were also externally validated by training the data on one hospital sample and testing on the other, and an overall estimated error was calculated using a completely random sample from both hospitals. Results: The average age of the patients was 66.7 (SD 17.2) years; 978 (54.3{\%}) out of 1821 patients were female. The logistic regression model achieved the best performance using a combination of ICD codes, medications, and notes, with an AUROC of 0.968 (95{\%} CI 0.940-0.982) and an AUPRC of 0.921 (95{\%} CI 0.835-0.969). The models that only used ICD codes or medications had lower performance. The estimated overall error rate in a random EHR sample was 1.6{\%}. The model also showed high external validity from training on Mass General Hospital data and testing on Beth Israel Deaconess Medical Center data (AUROC 0.927, 95{\%} CI 0.908-0.944) and vice versa (AUROC 0.968, 95{\%} CI 0.957-0.976). Conclusions: The proposed EHR-based phenotyping model for CHF achieved excellent performance, external validity, and generalization across two institutions. The model enables multiple downstream uses, paving the way for large-scale studies of CHF treatment effectiveness, comorbidities, outcomes, and mechanisms. ",
issn="2291-9694",
doi="10.2196/64113",
url="https://medinform.jmir.org/2025/1/e64113",
url="https://doi.org/10.2196/64113",
url="http://www.ncbi.nlm.nih.gov/pubmed/40208662"
}