title: A hybrid model for automatic identification of risk factors for heart disease.
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.
authors.affiliation: School of Computer Science, University of Nottingham, Nottingham, UK; Advanced Data Analysis Centre, University of Nottingham, Nottingham, UK. Electronic address: Hui.Yang@nottingham.ac.uk.
authors.forename: Hui
authors.initials: H
authors.lastname: Yang
authors.affiliation: School of Computer Science, University of Nottingham, Nottingham, UK; Advanced Data Analysis Centre, University of Nottingham, Nottingham, UK.
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Garibaldi
mesh_terms: D000368:Aged; D015331:Cohort Studies; D015897:Comorbidity; D016494:Computer Security; D003219:Confidentiality; D003324:Coronary Artery Disease / Q000175:diagnosis / Q000453:epidemiology*; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records / Q000458:organization & administration*; D005260:Female; D006801:Humans; D015994:Incidence; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D033262:Narration; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D018570:Risk Assessment / Q000379:methods; D006113:United Kingdom / Q000453:epidemiology; D018875:Vocabulary, Controlled
keywords: Clinical text mining; Heart disease; Hybrid model; Machine learning; Natural language processing; Risk factors; Rule-based approach