title: Automatic detection of protected health information from clinic narratives.
abstract: This paper presents a natural language processing (NLP) system that was designed to participate in the 2014 i2b2 de-identification challenge. The challenge task aims to identify and classify seven main Protected Health Information (PHI) categories and 25 associated sub-categories. A hybrid model was proposed which combines machine learning techniques with keyword-based and rule-based approaches to deal with the complexity inherent in PHI categories. Our proposed approaches exploit a rich set of linguistic features, both syntactic and word surface-oriented, which are further enriched by task-specific features and regular expression template patterns to characterize the semantics of various PHI categories. Our system achieved promising accuracy on the challenge test data with an overall micro-averaged F-measure of 93.6%, which was the winner of this de-identification challenge.
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: D002681:China; D015331:Cohort Studies; D016494:Computer Security; D003219:Confidentiality; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records / Q000458:organization & administration*; D033262:Narration; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D018875:Vocabulary, Controlled
keywords: Clinical text mining; De-identification; Hybrid model; Natural language processing; Protected Health Information (PHI)