Large-scale evaluation of automated clinical note de-identification and its impact on information extraction
Solti, ImreNote: Order does not necessarily reflect citation order of authors.
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CitationDeleger, L., K. Molnar, G. Savova, F. Xia, T. Lingren, Q. Li, K. Marsolo, et al. 2012. “Large-scale evaluation of automated clinical note de-identification and its impact on information extraction.” Journal of the American Medical Informatics Association : JAMIA 20 (1): 84-94. doi:10.1136/amiajnl-2012-001012. http://dx.doi.org/10.1136/amiajnl-2012-001012.
AbstractObjective: (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.
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