Identification of Patients with Congestive Heart Failure: Automated Electronic Health Records Phenotyping (Preprint)

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 (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 patients in whether they have CHF based on structured and unstructured data from each patient including medication, ICD codes, and information extracted through NLP of notes left by providers by comparing effectiveness of several machine learning models. METHODS We developed a NLP model to identify CHF from medical records using Electronic Health Record (EHR) from two hospitals (Mass General Hospital and Beth Israel Deaconess Medical Center from 2010 to 2023), using 2800 clinical visit notes from 1821 patients. We trained and compared the performance of logistic regression, random forest, and RoBERTa models. We measured model performance using areas under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC). These models were also externally validated by training the data on one hospital sample and testing on the other as well as an overall estimated error from a completely random sample from both hospitals was used. RESULTS Average age was 67.3 years old; 54.3% were female. The logistic regression model achieved the best performance using a combination of ICD codes, medications, and notes with an AUROC at 0.968 (0.940 – 0.982) and AUPRC at 0.921 (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 is 1.6%. The model also showed high external validitiy from training on MGH and testing on BIDMC (AUROC 0.927) and vise versa (AUROC 0.968). CONCLUSIONS The proposed CHF EHR phenotyping model achieved excellent performance, external validity, and generalized across two institutions. The model enables multiple downstream uses, paving the way for large-scale studies of CHF treatment effectiveness, comorbidities, outcomes, and mechanisms.