Automatic detection of protected health information from clinic narratives.
J Biomed Inform
; 58 Suppl: S30-S38, 2015 Dec.
Article
en En
| MEDLINE
| ID: mdl-26231070
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Reconocimiento de Normas Patrones Automatizadas
/
Seguridad Computacional
/
Confidencialidad
/
Narración
/
Registros Electrónicos de Salud
/
Minería de Datos
Tipo de estudio:
Diagnostic_studies
/
Etiology_studies
/
Incidence_studies
/
Observational_studies
/
Prognostic_studies
/
Qualitative_research
/
Risk_factors_studies
País/Región como asunto:
Asia
Idioma:
En
Revista:
J Biomed Inform
Asunto de la revista:
INFORMATICA MEDICA
Año:
2015
Tipo del documento:
Article