ArticleJournal of biomedical informatics2017
Automatic prediction of coronary artery disease from clinical narratives.
Article in Journal of biomedical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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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
17 citing papers in PubMed.
- Development and external validation of an interpretable machine-learning model for HFpEF comorbidity risk in COPD patients.Respiratory research · 2026Article
- Leveraging free-text clinical records for heart disease classification through structured feature mapping.PloS one · 2026Article
- Serum calcium-based interpretable machine learning model for predicting anastomotic leakage after rectal cancer resection: A multi-center study.World journal of gastroenterology · 2025Observational
- Machine Learning-Based Prediction of First Trimester Down Syndrome Risk in East Asian Populations.Risk management and healthcare policy · 2025Article
- Prospects for AI clinical summarization to reduce the burden of patient chart review.Frontiers in digital health · 2024Review
- Fusion Modeling: Combining Clinical and Imaging Data to Advance Cardiac Care.Circulation. Cardiovascular imaging · 2023Review
- 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
- Natural Language Processing: from Bedside to Everywhere.Yearbook of medical informatics · 2022Article
- Artificial Intelligence and Cardiovascular Genetics.Life (Basel, Switzerland) · 2022Review
- Multimodal temporal-clinical note network for mortality prediction.Journal of biomedical semantics · 2021Article
- Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach.PloS one · 2021Article
- The 2019 National Natural language processing (NLP) Clinical Challenges (n2c2)/Open Health NLP (OHNLP) shared task on clinical concept normalization for clinical records.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Prediction of blood culture outcome using hybrid neural network model based on electronic health records.BMC medical informatics and decision making · 2020Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.JMIR medical informatics · 2019Review
- Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview.The western journal of emergency medicine · 2019Review
- Artificial Intelligence for Anesthesia: What the Practicing Clinician Needs to Know: More than Black Magic for the Art of the Dark.Anesthesiology · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Coronary Artery Disease (CAD) is not only the most common form of heart disease, but also the leading cause of death in both men and women (Coronary Artery Disease: MedlinePlus, 2015). We present a system that is able to automatically predict whether patients develop coronary artery disease based on their narrative medical histories, i.e., clinical free text. Although the free text in medical records has been used in several studies for identifying risk factors of coronary artery disease, to the best of our knowledge our work marks the first attempt at automatically predicting development of CAD. We tackle this task on a small corpus of diabetic patients. The size of this corpus makes it important to limit the number of features in order to avoid overfitting. We propose an ontology-guided approach to feature extraction, and compare it with two classic feature selection techniques. Our system achieves state-of-the-art performance of 77.4% F1 score.
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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.