Carlton Moore, MD, professor of medicine in the division of infectious diseases, is first author of a retrospective observational study design of patients hospitalized in 2015 from four hospitals participating in the Atherosclerosis Risk in Communities (ARIC) study.
Using free-text clinical notes and reports from hospitalized patients, the study team wanted to determine the performance of natural language processing (NLP) ascertainment of Framingham heart failure (HF) criteria and phenotype.
Results showed performance of NLP ascertainment of Framingham HF phenotype in the validation dataset was good, with 78.8%, 81.7%, 84.4% and 80.0% for sensitivity, specificity, PPV and agreement, respectively. By decreasing the need for manual chart review, results on the use of NLP to ascertain Framingham HF phenotype from free-text electronic health record data suggest that validated NLP technology holds the potential for significantly improving the feasibility and efficiency of conducting large-scale epidemiologic surveillance of HF prevalence and incidence.