Dictionary construction and identification of possible adverse drug events in danish clinical narrative text

Journal of the American Medical Informatics Association

Volumn 20, Issue 5, 2013, Pages 947-953

  Eriksson, Robert ^a,b   Jensen, Peter Bjødstrup ^b   Frankild, Sune ^b   Jensen, Lars Juhl ^b   Brunak, Søren ^a,b  

^a UNIVERSITY OF COPENHAGEN   (Denmark)

^b TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ADVERSE DRUG REACTION; ARTICLE; ARTIFACT; BOOK; ELECTRONIC MEDICAL RECORD; HUMAN; INFORMATION PROCESSING; LANGUAGE; MEDICAL RECORD; MENTAL HOSPITAL; NARRATIVE; RECALL; RECOGNITION; VALIDATION PROCESS;

ADVERSE DRUG EVENT; ADVERSE DRUG REACTION REPORTING SYSTEMS; DATA MINING; DICTIONARY; ELECTRONIC HEALTH RECORDS;

DATA MINING; DENMARK; DICTIONARIES, MEDICAL; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; ELECTRONIC HEALTH RECORDS; HUMANS; NARRATION;

EID: 84882783145     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2013-001708     Document Type: Article

References (28)

1. World Health Organization. The importance of pharmacovigilance. Geneva, Switzerland: World Health Organization, 2002.
2. Bourgeois FT, Shannon MW, Valim C, et al. Adverse drug events in the outpatient setting: an 11-year national analysis. Pharmacoepidemiol Drug Saf 2010;19:901-10.
3. Budnitz DS, Pollock DA, Weidenbach KN, et al. National surveillance of emergency department visits for outpatient adverse drug events. JAMA 2006;296:1858-66.
4. Pirmohamed M, James S, Meakin S, et al. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients. BMJ 2004;329:15-9.
5. Rottenkolber D, Schmiedl S, Rottenkolber M, et al. Adverse drug reactions in Germany: direct costs of internal medicine hospitalizations. Pharmacoepidemiol Drug Saf 2011;20:626-34.
6. Mann RD, Andrews EB. Pharmacovigilance. 2nd edn. Chichester, England: John Wiley & Sons, 2007.
7. Strom BL, Kimmel SE, Hennessy S. Pharmacoepidemiology. 5th edn. Chicester, England: John Wiley & Sons, 2012.
8. Hazell L, Shakir SAW. Under-reporting of adverse drug reactions a systematic review. Drug Saf 2006;29:385-96.
9. 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;2008:128-44.
10. Pakhomov S, Pedersen T, Chute CG. Abbreviation and acronym disambiguation in clinical discourse. In: Proceedings of the AMIA Annu Symp Proc, 2005. American Medical Informatics Association (AMIA), 2005:589-93.
11. Liu H, Lussier YA, Friedman C. Disambiguating ambiguous biomedical terms in biomedical narrative text: an unsupervised method. J Biomed Inform 2001;34:249-61.
12. Friedman C, Alderson PO, Austin JHM, et al. A general natural-language text processor for clinical radiology. J Am Med Inform Assoc 1994;1:161-74.
13. 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;17:507-13.
14. Xu H, Stenner SP, Doan S, et al. MedEx: a medication information extraction system for clinical narratives. J Am Med Inform Assoc 2010;17:19-24.
15. 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.
16. Murff HJ, Forster AJ, Peterson JF, et al. Electronically screening discharge summaries for adverse medical events. J Am Med Inform Assoc 2003;10:339-50.
17. Wang X, Chase H, Markatou M, et al. Selecting information in electronic health records for knowledge acquisition. J Biomed Inform 2010;43:595-601.
18. Sohn S, Kocher J-PA, Chute CG, et al. Drug side effect extraction from clinical narratives of psychiatry and psychology patients. J Am Med Inform Assoc 2011;18: i144-9.
19. Honigman B, Lee J, Rothschild J, et al. Using computerized data to identify adverse drug events in outpatients. J Am Med Inform Assoc 2001;8:254-66.
20. The Uppsala Monitoring Centre. The WHO adverse reaction terminology- WHO-ART. Uppsala, Sweden: 2005.
21. Food and Drug Administration. COSTART: coding symbols for thesaurus of adverse reaction terms. 5th edn. Silver Spring, MD, USA: 1995.
22. Brown EG, Wood L, Wood S. The medical dictionary for regulatory activities (MedDRA). Drug Saf 1999;20:109-17.
23. Bick E. A named entity recognizer for Danish. In: Proceedings of the 4th International Conference on Language Resources and Evaluation, LREC 2004. European Language Resources Association, 2004:305-8.
24. Johannessen JB, Hagen K, Haaland å, et al. Named entity recognition for the mainland Scandinavian languages. Literary Linguistic Comput 2005; 20:91-102.
25. Roque FS, Jensen PB, Schmock H, et al. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput Biol 2011;7: e1002141.
26. Skeppstedt M, Kvist M, Dalianis H. Rule-based entity recognition and coverage of SNOMED CT in Swedish clinical text. In: Proceedings of the 8th International Conference on Language Resources and Evaluation, LREC 2012. European Language Resources Association, 2012:1250-7.
27. Gremse M, Chang A, Schomburg I, et al. The BRENDA Tissue Ontology (BTO): the first all-integrating ontology of all organisms for enzyme sources. Nucleic Acids Res 2011;39:D507-513.
28. Pafilis E, O'Donoghue SI, Jensen LJ, et al. Reflect: augmented browsing for the life scientist. Nat Biotechnol 2009;27:508-10.