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. 2015 Dec;58 Suppl(Suppl):S30-S38.
doi: 10.1016/j.jbi.2015.06.015. Epub 2015 Jul 29.

Automatic detection of protected health information from clinic narratives

Hui Yang  1 , Jonathan M Garibaldi  2
Affiliations

Automatic detection of protected health information from clinic narratives

Hui Yang et al. J Biomed Inform. 2015 Dec.

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.

Keywords: Clinical text mining; De-identification; Hybrid model; Natural language processing; Protected Health Information (PHI).

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Figures

Figure 1
Figure 1
Example of clinical record with annotated PHI categories
Figure 2
Figure 2
System Diagram for the De-identification Task
See this image and copyright information in PMC

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