Identification of Recurrent Atrial Fibrillation using Natural Language Processing Applied to Electronic Health Records.
Chengyi ZhengMing-Sum LeeNisha BansalAlan S GoCheng ChenTeresa N HarrisonDongjie FanAmanda AllenElisha GarciaBen LidgardDaniel SingerJaejin AnPublished in: European heart journal. Quality of care & clinical outcomes (2023)
When compared to 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.
Keyphrases
- atrial fibrillation
- electronic health record
- machine learning
- deep learning
- left atrial
- oral anticoagulants
- randomized controlled trial
- catheter ablation
- direct oral anticoagulants
- left atrial appendage
- clinical decision support
- autism spectrum disorder
- high throughput
- physical activity
- percutaneous coronary intervention
- venous thromboembolism
- single cell
- coronary artery disease
- smoking cessation
- replacement therapy