ArticleJournal of biomedical informatics2015
Coronary artery disease risk assessment from unstructured electronic health records using text mining.
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 27 papers, 2 of them syntheses that pooled it.
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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
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic review of conceptual and theoretical frameworks used in palliative care and end-of-life care research studies.Palliative medicine · 2023Pooled it
- Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2021Pooled it
- An explainable machine learning model for predicting chronic coronary disease and identifying valuable text features.Frontiers in cardiovascular medicine · 2025Article
- Use of SNOMED CT in Large Language Models: Scoping Review.JMIR medical informatics · 2024Article
- Predicted cardiovascular disease risk and prescribing of antihypertensive therapy among patients with hypertension in Australia using MedicineInsight.Journal of human hypertension · 2023Article
- Automated Detection of Substance-Use Status and Related Information from Clinical Text.Sensors (Basel, Switzerland) · 2022Article
- Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction.Sensors (Basel, Switzerland) · 2022Article
- Prediction of acute appendicitis among patients with undifferentiated abdominal pain at emergency department.BMC medical research methodology · 2022Article
- MLEE: A method for extracting object-level medical knowledge graph entities from Chinese clinical records.Frontiers in genetics · 2022Article
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- iCovidCare: Intelligent health monitoring framework for COVID-19 using ensemble random forest in edge networks.Internet of things (Amsterdam, Netherlands) · 2021Article
- Large-scale identification of aortic stenosis and its severity using natural language processing on electronic health records.Cardiovascular digital health journal · 2021Article
- Text Mining of Electronic Health Records Can Accurately Identify and Characterize Patients With Systemic Lupus Erythematosus.ACR open rheumatology · 2021Article
- Different Data Mining Approaches Based Medical Text Data.Journal of healthcare engineering · 2021Review
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Big data analytics for preventive medicine.Neural computing & applications · 2020Article
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.JMIR medical informatics · 2019Review
- Merging Data Diversity of Clinical Medical Records to Improve Effectiveness.International journal of environmental research and public health · 2019Article
- Automatic prediction of coronary artery disease from clinical narratives.Journal of biomedical informatics · 2017Article
- Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text.Yearbook of medical informatics · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Coronary artery disease (CAD) often leads to myocardial infarction, which may be fatal. Risk factors can be used to predict CAD, which may subsequently lead to prevention or early intervention. Patient data such as co-morbidities, medication history, social history and family history are required to determine the risk factors for a disease. However, risk factor data are usually embedded in unstructured clinical narratives if the data is not collected specifically for risk assessment purposes. Clinical text mining can be used to extract data related to risk factors from unstructured clinical notes. This study presents methods to extract Framingham risk factors from unstructured electronic health records using clinical text mining and to calculate 10-year coronary artery disease risk scores in a cohort of diabetic patients. We developed a rule-based system to extract risk factors: age, gender, total cholesterol, HDL-C, blood pressure, diabetes history and smoking history. The results showed that the output from the text mining system was reliable, but there was a significant amount of missing data to calculate the Framingham risk score. A systematic approach for understanding missing data was followed by implementation of imputation strategies. An analysis of the 10-year Framingham risk scores for coronary artery disease in this cohort has shown that the majority of the diabetic patients are at moderate risk of CAD.
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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.