Vaccine adverse event text mining system for extracting features from vaccine safety reports

Journal of the American Medical Informatics Association

Volumn 19, Issue 6, 2012, Pages 1011-1018

  Botsis, Taxiarchis ^a,b   Buttolph, Thomas ^a   Nguyen, Michael ^a   Winiecki, Scott ^a   Woo, Emily Jane ^a   Ball, Robert ^a  

^a CENTER FOR BIOLOGICS EVALUATION AND RESEARCH   (United States)

^b UNIVERSITY OF TROMSØ   (Norway)

Author keywords

[No Author keywords available]

Indexed keywords

VACCINE;

ACCURACY; ARTICLE; CAUSE OF DEATH; CLINICAL FEATURE; DRUG SAFETY; DRUG SURVEILLANCE PROGRAM; INFORMATION RETRIEVAL; SENSITIVITY AND SPECIFICITY;

ADVERSE DRUG REACTION REPORTING SYSTEMS; DATA MINING; HUMANS; NATURAL LANGUAGE PROCESSING; SEMANTICS; UNITED STATES; VACCINES;

EID: 84867670536     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2012-000881     Document Type: Article

References (28)

1. Food and Drug Administration. Guidance for Industry: Good Pharmacovigilance Practices and Pharmacoepidemiologic Assessment. Rockville: Food and Drug Administration, 2005.
2. Melton GB, Hripcsak G. Automated detection of adverse events using natural language processing of discharge summaries. J Am Med Inform Assoc 2005;12:448e57.
3. Penz JF, Wilcox AB, Hurdle JF. Automated identification of adverse events related to central venous catheters. J Biomed Inform 2007;40:174e82.
4. Mykowiecka A, Marciniak M, Kupsc A. Rule-based information extraction from patients' clinical data. J Biomed Inform 2009;42:923e36.
5. Meystre SM, Savova GK, Kipper-Schuler KC, et al. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform 2008:128e44.
6. Savova GK, Masanz JJ, Ogren PV, et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 2010;17:507e13.
7. Sebastiani F. Machine learning in automated text categorization. ACM computing surveys (CSUR) 2002;34:47.
8. Kim JD, Ohta T, Tsujii J. Corpus annotation for mining biomedical events from literature. BMC Bioinformatics 2008;9:10.
9. Coden A, Savova G, Sominsky I, et al. Automatically extracting cancer disease characteristics from pathology reports into a disease knowledge representation model. J Biomed Inform 2009;42:937e49.
10. Friedman C. A broad-coverage natural language processing system. Proc AMIA Symp 2000:270e4.
11. Varricchio F, Iskander J, Destefano F, et al. Understanding vaccine safety information from the Vaccine Adverse Event Reporting System. Pediatr Infect Dis J 2004;23:287e94.
12. Friedman C, Alderson PO, Austin JH, et al. A general natural-language text processor for clinical radiology. J Am Med Inform Assoc 1994;1:161e74.
13. Herman TD, Liu F, Sagaram D, et al. Creating a vaccine adverse event ontology for public health. AMIA Annu Symp Proc 2005:978.
14. Chang A, Schyve PM, Croteau RJ, et al. The JCAHO patient safety event taxonomy: a standardized terminology and classification schema for near misses and adverse events. Int J Qual Health Care 2005;17:95e105.
15. Spasic I, Ananiadou S, McNaught J, et al. Text mining and ontologies in biomedicine: making sense of raw text. Brief Bioinform 2005;6:239e51.
16. Domino FJ, Griffith HW. The 5-minute Clinical Consult. Philadelphia, PA: Lippincott Williams & Wilkins, 2010.
17. LeBlond RF, DeGowin RL, Brown DD. DeGowin's Diagnostic Examination. Columbus, OH: McGraw-Hill Professional, 2008.
18. Drugs@FDA Data Files. http://www.fda.gov/Drugs/InformationOnDrugs/ucm079750.htm (accessed 1 Oct 2010).
19. Xu H, Stenner SP, Doan S, et al. MedEx: a medication information extraction system for clinical narratives. J Am Med Inform Assoc 2010;17:19e24.
20. Friedman C, Shagina L, Lussier Y, et al. Automated encoding of clinical documents based on natural language processing. J Am Med Inform Assoc 2004;11:392e402.
21. Chapman WW, Bridewell W, Hanbury P, et al. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform 2001;34:301e10.
22. Harkema H, Dowling JN, Thornblade T, et al. ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports. J Biomed Inform 2009;42:839e51.
23. Zeng QT, Goryachev S, Weiss S, et al. Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system. BMC Med Inform Decis Mak 2006;6:30.
24. Roberts A, Gaizauskas R, Hepple M, et al. Building a semantically annotated corpus of clinical texts. J Biomed Inform 2009;42:950e66.
25. Botsis T, Nguyen MD, Woo EJ, et al. Text mining for the Vaccine Adverse Event Reporting System: medical text classification using informative feature selection. J Am Med Inform Assoc 2011;18:631e8.
26. Ruggeberg JU, Gold MS, Bayas JM, et al. Anaphylaxis: case definition and guidelines for data collection, analysis, and presentation of immunization safety data. Vaccine 2007;25:5675e84.
27. Manning CD, Raghavan P, Schutze H. Introduction to Information Retrieval, Vol. 1. New York, NY: Cambridge University Press, 2008.
28. ABC tool: Electronic Concultant. http://brightoncollaboration.org/public/what-we-do/ capacity/abc.html (accessed 14 Jun 2012).