ArticleJournal of biomedical informatics2015
Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes.
Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2021Pooled it
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
- Machine Learning and Natural Language Processing to Improve Classification of Atrial Septal Defects in Electronic Health Records.Birth defects research · 2025Article
- Multimodal Data Hybrid Fusion and Natural Language Processing for Clinical Prediction Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Development and Evaluation of a Natural Language Processing System for Curating a Trans-Thoracic Echocardiogram (TTE) Database.Bioengineering (Basel, Switzerland) · 2023Article
- Supervised Text Classification System Detects Fontan Patients in Electronic Records With Higher Accuracy ThanJournal of the American Heart Association · 2023Article
- Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques.Scientific reports · 2023Article
- Analysis of 'One in a Million' primary care consultation conversations using natural language processing.BMJ health & care informatics · 2023Article
- Development and external validation of a machine learning-based prediction model for the cancer-related fatigue diagnostic screening in adult cancer patients: a cross-sectional study in China.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2023Article
- Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records.Methods of information in medicine · 2022Article
- Natural language processing for identification of hypertrophic cardiomyopathy patients from cardiac magnetic resonance reports.BMC medical informatics and decision making · 2022Article
- Identify diabetic retinopathy-related clinical concepts and their attributes using transformer-based natural language processing methods.BMC medical informatics and decision making · 2022Article
- Tasks as needs: reframing the paradigm of clinical natural language processing research for real-world decision support.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Poor Practitioner Adherence to Clinical Tobacco Use Guidelines in Liver Transplant Recipients.Transplantation direct · 2022Article
- Natural language processing for the assessment of cardiovascular disease comorbidities: The cardio-Canary comorbidity project.Clinical cardiology · 2021Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Natural language processing of lifestyle modification documentation.Health informatics journal · 2020Article
- Blood pressure control according to clinical practice guidelines is associated with decreased mortality and cardiovascular events among liver transplant recipients.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2020Article
- Big data in status epilepticus.Epilepsy & behavior : E&B · 2019Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The 2014 i2b2 natural language processing shared task focused on identifying cardiovascular risk factors such as high blood pressure, high cholesterol levels, obesity and smoking status among other factors found in health records of diabetic patients. In addition, the task involved detecting medications, and time information associated with the extracted data. This paper presents the development and evaluation of a natural language processing (NLP) application conceived for this i2b2 shared task. For increased efficiency, the application main components were adapted from two existing NLP tools implemented in the Apache UIMA framework: Textractor (for dictionary-based lookup) and cTAKES (for preprocessing and smoking status detection). The application achieved a final (micro-averaged) F1-measure of 87.5% on the final evaluation test set. Our attempt was mostly based on existing tools adapted with minimal changes and allowed for satisfying performance with limited development efforts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.