ArticleEuropean heart journal. Quality of care & clinical outcomes2024
Identification of recurrent atrial fibrillation using natural language processing applied to electronic health records.
Article in European heart journal. Quality of care & clinical outcomes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 22 citations in OpenAlex.
- Leveraging electronic health records for atrial fibrillation cohort generation.Health information science and systems · 2026Article
- Clinical and socioeconomic factors associated with recurrent atrial fibrillation after catheter ablation or antiarrhythmic drug therapy.Heart rhythm O2 · 2025Article
- Hybrid natural language processing tool for semantic annotation of medical texts in Spanish.BMC bioinformatics · 2025Article
- Engineering of Generative Artificial Intelligence and Natural Language Processing Models to Accurately Identify Arrhythmia Recurrence.Circulation. Arrhythmia and electrophysiology · 2025Article
- A Scoping Review of the Use of Artificial Intelligence in the Identification and Diagnosis of Atrial Fibrillation.Journal of personalized medicine · 2024Article
- Natural Language Processing Versus Diagnosis Code-Based Methods for Postherpetic Neuralgia Identification: Algorithm Development and Validation.JMIR medical informatics · 2024Article
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Authors and funding
12 authors at 5 institutions in 1 country.
Funding
Abstract
aimsThis study aimed to develop and apply natural language processing (NLP) algorithms to identify recurrent atrial fibrillation (AF) episodes following rhythm control therapy initiation using electronic health records (EHRs). METHODS AND
resultsWe included adults with new-onset AF who initiated rhythm control therapies (ablation, cardioversion, or antiarrhythmic medication) within two US integrated healthcare delivery systems. A code-based algorithm identified potential AF recurrence using diagnosis and procedure codes. An automated NLP algorithm was developed and validated to capture AF recurrence from electrocardiograms, cardiac monitor reports, and clinical notes. Compared with the reference standard cases confirmed by physicians' adjudication, the F-scores, sensitivity, and specificity were all above 0.90 for the NLP algorithms at both sites. We applied the NLP and code-based algorithms to patients with incident AF (n = 22 970) during the 12 months after initiating rhythm control therapy. Applying the NLP algorithms, the percentages of patients with AF recurrence for sites 1 and 2 were 60.7% and 69.9% (ablation), 64.5% and 73.7% (cardioversion), and 49.6% and 55.5% (antiarrhythmic medication), respectively. In comparison, the percentages of patients with code-identified AF recurrence for sites 1 and 2 were 20.2% and 23.7% for ablation, 25.6% and 28.4% for cardioversion, and 20.0% and 27.5% for antiarrhythmic medication, respectively.
conclusionWhen compared with a code-based approach alone, this study's high-performing automated NLP method identified significantly more patients with recurrent AF. The NLP algorithms could enable efficient evaluation of treatment effectiveness of AF therapies in large populations and help develop tailored interventions.
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