Automatic detection of protected health information from clinic narratives
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...
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| Format: | Article |
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Elsevier
2015
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| Online Access: | https://eprints.nottingham.ac.uk/37551/ |
| _version_ | 1848795482515570688 |
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| author | Yang, Hui Garibaldi, Jonathan M. |
| author_facet | Yang, Hui Garibaldi, Jonathan M. |
| author_sort | Yang, Hui |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:32:47Z |
| format | Article |
| id | nottingham-37551 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:32:47Z |
| publishDate | 2015 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-375512020-05-04T17:12:19Z https://eprints.nottingham.ac.uk/37551/ Automatic detection of protected health information from clinic narratives Yang, Hui Garibaldi, Jonathan M. 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. Elsevier 2015-07-29 Article PeerReviewed Yang, Hui and Garibaldi, Jonathan M. (2015) Automatic detection of protected health information from clinic narratives. Journal of Biomedical Informatics, 58 (Suppl.). S30-S38. ISSN 1532-0480 Protected Health Information (PHI); De-identification; Hybrid model; Natural language processing; Clinical text mining http://www.sciencedirect.com/science/article/pii/S1532046415001252 doi:10.1016/j.jbi.2015.06.015 doi:10.1016/j.jbi.2015.06.015 |
| spellingShingle | Protected Health Information (PHI); De-identification; Hybrid model; Natural language processing; Clinical text mining Yang, Hui Garibaldi, Jonathan M. Automatic detection of protected health information from clinic narratives |
| title | Automatic detection of protected health information from clinic narratives |
| title_full | Automatic detection of protected health information from clinic narratives |
| title_fullStr | Automatic detection of protected health information from clinic narratives |
| title_full_unstemmed | Automatic detection of protected health information from clinic narratives |
| title_short | Automatic detection of protected health information from clinic narratives |
| title_sort | automatic detection of protected health information from clinic narratives |
| topic | Protected Health Information (PHI); De-identification; Hybrid model; Natural language processing; Clinical text mining |
| url | https://eprints.nottingham.ac.uk/37551/ https://eprints.nottingham.ac.uk/37551/ https://eprints.nottingham.ac.uk/37551/ |