ArticleJournal of biomedical informatics2015
A hybrid model for automatic identification of risk factors for heart disease.
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 18 papers.
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Who cites it
18 citing papers in PubMed.
- An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction.Diagnostics (Basel, Switzerland) · 2024Article
- Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques.Scientific reports · 2023Article
- Automated Detection of Substance-Use Status and Related Information from Clinical Text.Sensors (Basel, Switzerland) · 2022Article
- An Efficient Machine Learning Model Based on Improved Features Selections for Early and Accurate Heart Disease Predication.Computational intelligence and neuroscience · 2022Article
- An ML-Enabled Internet of Things Framework for Early Detection of Heart Disease.BioMed research international · 2022Article
- Early recognition of risk of critical adverse events based on deep neural decision gradient boosting.Frontiers in public health · 2022Article
- Measuring Adoption of Patient Priorities-Aligned Care Using Natural Language Processing of Electronic Health Records: Development and Validation of the Model.JMIR medical informatics · 2021Article
- Early and accurate detection and diagnosis of heart disease using intelligent computational model.Scientific reports · 2020Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Recurrent Neural Networks for Early Detection of Heart Failure From Longitudinal Electronic Health Record Data: Implications for Temporal Modeling With Respect to Time Before Diagnosis, Data Density, Data Quantity, and Data Type.Circulation. Cardiovascular quality and outcomes · 2019Article
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.JMIR medical informatics · 2019Review
- Robust clinical marker identification for diabetic kidney disease with ensemble feature selection.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Assessing Information Congruence of Documented Cardiovascular Disease between Electronic Dental and Medical Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Automatic prediction of coronary artery disease from clinical narratives.Journal of biomedical informatics · 2017Article
- Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome.International journal of environmental research and public health · 2016Article
- Big data analytics to improve cardiovascular care: promise and challenges.Nature reviews. Cardiology · 2016Review
- Practical applications for natural language processing in clinical research: The 2014 i2b2/UTHealth shared tasks.Journal of biomedical informatics · 2015Article
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Authors and funding
2 authors.
Funding
Abstract
Coronary artery disease (CAD) is the leading cause of death in both the UK and worldwide. The detection of related risk factors and tracking their progress over time is of great importance for early prevention and treatment of CAD. This paper describes an information extraction system that was developed to automatically identify risk factors for heart disease in medical records while the authors participated in the 2014 i2b2/UTHealth NLP Challenge. Our approaches rely on several nature language processing (NLP) techniques such as machine learning, rule-based methods, and dictionary-based keyword spotting to cope with complicated clinical contexts inherent in a wide variety of risk factors. Our system achieved encouraging performance on the challenge test data with an overall micro-averaged F-measure of 0.915, which was competitive to the best system (F-measure of 0.927) of this challenge task.
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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.