A hybrid NLP system combining machine learning, rule-based methods, and dictionary-based keyword spotting achieved an overall micro-averaged F-measure of 0.915 for identifying heart disease risk factors.
A hybrid NLP system can effectively identify heart disease risk factors from medical records with high accuracy.
Absolute Event Rate: 0.915% vs 0.927%
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.
Yang et al. (Sun,) conducted a other in Coronary artery disease risk factors. Hybrid NLP information extraction system vs. Best system in the challenge was evaluated on Micro-averaged F-measure for identifying risk factors. A hybrid NLP system combining machine learning, rule-based methods, and dictionary-based keyword spotting achieved an overall micro-averaged F-measure of 0.915 for identifying heart disease risk factors.