ArticleClinical cardiology2021
Natural language processing for the assessment of cardiovascular disease comorbidities: The cardio-Canary comorbidity project.
Article in Clinical cardiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- How to assess multimorbidity: a systematic review.Frontiers in public health · 2025Pooled it
- Systematic Review of Large Language Models and Natural Language Processing in Stroke Care: Applications, Challenges, and Future Directions.Stroke (Hoboken, N.J.) · 2026Review
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
- Utilizing large language models and natural language processing to classify ischemia status from cardiac stress tests in a large multicenter healthcare system.BMC research notes · 2025Article
- Aortic valve replacement and mortality in asymptomatic individuals with severe aortic stenosis and left ventricular hypertrophy.European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery · 2025Article
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- Sex Differences in the Association Between Lipoprotein(a) and Cardiovascular Outcomes: The MGB Lp(a) Registry.Journal of the American Heart Association · 2025Article
- EHR-Based Screening of Familial Hypercholesterolemia: Finding the Lipid in the Haystack.JACC. Advances · 2024Article
- Social Phenotyping for Cardiovascular Risk Stratification in Electronic Health Registries.Current atherosclerosis reports · 2024Review
- Lipoprotein(a) as a cardiovascular risk factor among patients with and without diabetes Mellitus: the Mass General Brigham Lp(a) Registry.Cardiovascular diabetology · 2024Article
- Collaborative and privacy-enhancing workflows on a clinical data warehouse: an example developing natural language processing pipelines to detect medical conditions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Article
- Using natural language processing for automated classification of disease and to identify misclassified ICD codes in cardiac disease.European heart journal. Digital health · 2024Article
- Lipoprotein(a) and Major Adverse Cardiovascular Events in Patients With or Without Baseline Atherosclerotic Cardiovascular Disease.Journal of the American College of Cardiology · 2024Article
- Big Data, Big Insights: Leveraging Data Analytics to Unravel Cardiovascular Exposome Complexities.Methodist DeBakey cardiovascular journal · 2024Review
- Applications of the Natural Language Processing Tool ChatGPT in Clinical Practice: Comparative Study and Augmented Systematic Review.JMIR medical informatics · 2023Article
- Article
- Survey on natural language processing in medical image analysis.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2022Article
- Natural language processing for the assessment of cardiovascular disease comorbidities: The cardio-Canary comorbidity project.Clinical cardiology · 2021Article
- Natural language processing to phenotype coronary computed tomography angiography: Development, validation, and initial results of a large multi-institution cohort.Journal of cardiovascular computed tomographyArticle
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Authors and funding
13 authors.
Funding
Abstract
objectiveAccurate ascertainment of comorbidities is paramount in clinical research. While manual adjudication is labor-intensive and expensive, the adoption of electronic health records enables computational analysis of free-text documentation using natural language processing (NLP) tools. HYPOTHESIS: We sought to develop highly accurate NLP modules to assess for the presence of five key cardiovascular comorbidities in a large electronic health record system.
methodsOne-thousand clinical notes were randomly selected from a cardiovascular registry at Mass General Brigham. Trained physicians manually adjudicated these notes for the following five diagnostic comorbidities: hypertension, dyslipidemia, diabetes, coronary artery disease, and stroke/transient ischemic attack. Using the open-source Canary NLP system, five separate NLP modules were designed based on 800 "training-set" notes and validated on 200 "test-set" notes.
resultsAcross the five NLP modules, the sentence-level and note-level sensitivity, specificity, and positive predictive value was always greater than 85% and was most often greater than 90%. Accuracy tended to be highest for conditions with greater diagnostic clarity (e.g. diabetes and hypertension) and slightly lower for conditions whose greater diagnostic challenges (e.g. myocardial infarction and embolic stroke) may lead to less definitive documentation.
conclusionWe designed five open-source and highly accurate NLP modules that can be used to assess for the presence of important cardiovascular comorbidities in free-text health records. These modules have been placed in the public domain and can be used for clinical research, trial recruitment and population management at any institution as well as serve as the basis for further development of cardiovascular NLP tools.
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