Paper Info
Title
Large-scale evaluation of automated clinical note de-identification and its impact on information extraction.
Abstract
Objective (1) To evaluate a state-of-the-art natural language processing (NLP)-based approach to automatically de-identify a large set of diverse clinical notes. (2) To measure the impact of de-identification on the performance of information extraction algorithms on the de-identified documents. Material and methods A cross-sectional study that included 3503 stratified, randomly selected clinical notes (over 22 note types) from five million documents produced at one of the largest US pediatric hospitals. Sensitivity, precision, F value of two automated de-identification systems for removing all 18 HIPAA-defined protected health information elements were computed. Performance was assessed against a manually generated 'gold standard'. Statistical significance was tested. The automated de-identification performance was also compared with that of two humans on a 10% subsample of the gold standard. The effect of de-identification on the performance of subsequent medication extraction was measured. Results The gold standard included 30 815 protected health information elements and more than one million tokens. The most accurate NLP method had 91.92% sensitivity (R) and 95.08% precision (P) overall. The performance of the system was indistinguishable from that of human annotators (annotators' performance was 92.15%(R)/93.95%(P) and 94.55%(R)/88.45%(P) overall while the best system obtained 92.91%(R)/95.73%(P) on same text). The impact of automated de-identification was minimal on the utility of the narrative notes for subsequent information extraction as measured by the sensitivity and precision of medication name extraction. Discussion and conclusion NLP-based de-identification shows excellent performance that rivals the performance of human annotators. Furthermore, unlike manual de-identification, the automated approach scales up to millions of documents quickly and inexpensively.
Year
DOI
Venue
2013
10.1136/amiajnl-2012-001012
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
Keywords
Field
DocType
biomedical informatics,natural language processing,bionlp,text mining,translational medicine,medical informatics,protected health information,drug repositioning,nlp
Data mining,Health Insurance Portability and Accountability Act,Computer science,Natural language processing,Artificial intelligence,Medication name,De-identification,Information retrieval,Protected health information,Biomedical text mining,Information extraction,Deidentification,Health informatics
Journal
Volume
Issue
ISSN
20
1
1067-5027
Citations 
PageRank 
References 
16
0.92
24
Authors
11
Name
Order
Citations
PageRank
Louise Deleger123420.13
Katalin Molnar2624.60
Guergana Savova326725.03
Fei Xia418014.23
Todd Lingren511412.78
Qi Li6656.98
Keith Marsolo79414.24
Anil G Jegga841724.88
Megan Kaiser9927.44
Laura Stoutenborough10826.09
Imre Solti1133723.36