ArticleJournal of biomedical informatics2015
Annotating risk factors for heart disease in clinical narratives for diabetic patients.
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 24 papers.
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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.
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Who cites it
24 citing papers in PubMed, 65 citations in OpenAlex.
- Extracting Social Determinants of Health from Dental Clinical Notes.Applied clinical informatics · 2025Article
- Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques.Scientific reports · 2023Article
- SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks.Journal of biomedical semantics · 2022Article
- Multi-Level Representation Learning for Chinese Medical Entity Recognition: Model Development and Validation.JMIR medical informatics · 2020Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Cohort selection for clinical trials: n2c2 2018 shared task track 1.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Quantifying risk factors in medical reports with a context-aware linear model.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources.JMIR medical informatics · 2018Article
- Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives.PloS one · 2018Article
- Developing a cardiovascular disease risk factor annotated corpus of Chinese electronic medical records.BMC medical informatics and decision making · 2017Article
- Automatic prediction of coronary artery disease from clinical narratives.Journal of biomedical informatics · 2017Article
- Agile text mining for the 2014 i2b2/UTHealth Cardiac risk factors challenge.Journal of biomedical informatics · 2015Article
- Practical applications for natural language processing in clinical research: The 2014 i2b2/UTHealth shared tasks.Journal of biomedical informatics · 2015Article
- Creation of a new longitudinal corpus of clinical narratives.Journal of biomedical informatics · 2015Article
- A context-aware approach for progression tracking of medical concepts in electronic medical records.Journal of biomedical informatics · 2015Article
- The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs.Journal of biomedical informatics · 2015Article
- A hybrid model for automatic identification of risk factors for heart disease.Journal of biomedical informatics · 2015Article
- An automatic system to identify heart disease risk factors in clinical texts over time.Journal of biomedical informatics · 2015Article
- Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes.Journal of biomedical informatics · 2015Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
The 2014 i2b2/UTHealth natural language processing shared task featured a track focused on identifying risk factors for heart disease (specifically, Cardiac Artery Disease) in clinical narratives. For this track, we used a "light" annotation paradigm to annotate a set of 1304 longitudinal medical records describing 296 patients for risk factors and the times they were present. We designed the annotation task for this track with the goal of balancing annotation load and time with quality, so as to generate a gold standard corpus that can benefit a clinically-relevant task. We applied light annotation procedures and determined the gold standard using majority voting. On average, the agreement of annotators with the gold standard was above 0.95, indicating high reliability. The resulting document-level annotations generated for each record in each longitudinal EMR in this corpus provide information that can support studies of progression of heart disease risk factors in the included patients over time. These annotations were used in the Risk Factor track of the 2014 i2b2/UTHealth shared task. Participating systems achieved a mean micro-averaged F1 measure of 0.815 and a maximum F1 measure of 0.928 for identifying these risk factors in patient records.
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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.