ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2017
Automatic Methods to Extract New York Heart Association Classification from Clinical Notes.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Clinical phenotypes among patients that underwent cardiac resynchronization therapy using unsupervised learning integrating gated SPECT.Nuclear medicine communications · 2025Article
- Predicting New York Heart Association (NYHA) heart failure classification from medical student notes following simulated patient encounters.Scientific reports · 2025Article
- Deep learning models for predicting heart disease risk using the UCI database: methods, performance, and clinical context.American journal of cardiovascular disease · 2025Article
- Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure.JAMA network open · 2024Article
- A Deep Learning Approach for Automated Extraction of Functional Status and New York Heart Association Class for Heart Failure Patients During Clinical Encounters.medRxiv : the preprint server for health sciences · 2024Article
- Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation.IEEE open journal of engineering in medicine and biology · 2022Article
- Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning.Computational intelligence and neuroscience · 2021Article
- Discovering and identifying New York heart association classification from electronic health records.BMC medical informatics and decision making · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Cardiac Resynchronization Therapy (CRT) is an established pacing therapy for heart failure patients. The New York Heart Association (NYHA) classification is often used as a measure of a patient's response to CRT. Identifying NYHA class for heart failure patients in an electronic health record (EHR) consistently, over time, can provide better understanding of the progression of heart failure and assessment of CRT response and effectiveness. However, NYHA is rarely stored in EHR structured data such information is often documented in unstructured clinical notes. In this study, we thus investigated the use of natural language processing (NLP) methods to identify NYHA classification from clinical notes. We collected 6,174 clinical notes that were matched with hospital-specific custom NYHA class diagnosis codes. Machine-learning based methods performed similar with a rule-based method. The best machine-learning method, support vector machine with n-gram features, performed the best (93% F-measure). Further validation of the findings is required.
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Registered trials
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