ContextD: An algorithm to identify contextual properties of medical terms in a dutch clinical corpus

BMC Bioinformatics

Volumn 15, Issue 1, 2014, Pages

  Afzal, Zubair ^a   Pons, Ewoud ^a   Kang, Ning ^a   Sturkenboom, Miriam CJM ^a   Schuemie, Martijn J ^b   Kors, Jan A ^a  

^a ERASMUS MEDICAL CENTER   (Netherlands)

^b JANSSEN RESEARCH AND DEVELOPMENT   (United States)

Author keywords

Contextual features; Dutch electronic medical records; Negation detection

Indexed keywords

AUTOMATION; DIAGNOSIS; MEDICAL COMPUTING;

ALGORITHM DEVELOPMENT; AUTOMATIC IDENTIFICATION; CONTEXT ALGORITHM; CONTEXTUAL FEATURE; CONTEXTUAL PROPERTIES; ELECTRONIC MEDICAL RECORD; MEDICAL TERMS; PROPERTY VALUE;

ALGORITHMS;

ALGORITHM; ELECTRONIC MEDICAL RECORD; FACTUAL DATABASE; HUMAN; MEDICAL INFORMATICS; NATURAL LANGUAGE PROCESSING; NETHERLANDS; PROCEDURES; UNIFIED MEDICAL LANGUAGE SYSTEM;

ALGORITHMS; DATABASES, FACTUAL; HUMANS; MEDICAL INFORMATICS; MEDICAL RECORDS SYSTEMS, COMPUTERIZED; NATURAL LANGUAGE PROCESSING; NETHERLANDS; UNIFIED MEDICAL LANGUAGE SYSTEM;

EID: 84923933223     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-014-0373-3     Document Type: Article

References (38)

1. Jensen PB, Jensen LJ, Brunak S: Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet 2012, 13(June):395-405.
2. Friedman C, Hripcsak G: Natural language processing and its future in medicine. Acad Med 1999, 74:890-895.
3. Friedman C, Alderson PO, Austin JHM, Cimino JJ, Johnson SB: A general natural-language text processor for clinical radiology. J Am Med Informat Assoc 1994, 1:161-174.
4. Christensen LM, Haug PJ, Fiszman M: MPLUS: a probabilistic medical language understanding system. In Proc ACL-02 Work Nat Lang Process Biomed domain -, Volume 3. Morristown, NJ, USA: Association for Computational Linguistics; 2002:29-36.
5. Hahn U, Romacker M, Schulz S: MEDSYNDIKATE-a natural language system for the extraction of medical information from findings reports. Int J Med Inform 2002, 67:63-74.
6. Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, Chute CG: Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 2010, 17:507-513.
7. Xu H, Stenner SP, Doan S, Johnson KB, Waitman LR, Denny JC: MedEx: a medication information extraction system for clinical narratives. J Am Med Inform Assoc 2010, 17:19-24.
8. Zeng QT, Goryachev S, Weiss S, Sordo M, Murphy SN, Lazarus R: 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.
9. Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG: A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform 2001, 34:301-310.
10. Mutalik PG, Deshpande A, Nadkarni PM: Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS. J Am Med Inform Assoc 2001, 8:598-609.
11. Elkin PL, Brown SH, Bauer BA, Husser CS, Carruth W, Bergstrom LR, Wahner-Roedler DL: A controlled trial of automated classification of negation from clinical notes. BMC Med Inform Decis Mak 2005, 5:13.
12. Huang Y, Lowe HJ: A novel hybrid approach to automated negation detection in clinical radiology reports. J Am Med Inform Assoc 2007, 14:304-311.
13. Harkema H, Dowling JN, Thornblade T, Chapman WW: ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports. J Biomed Inform 2009, 42:839-851.
14. Kilicoglu H, Bergler S: Recognizing speculative language in biomedical research articles: a linguistically motivated perspective. BMC Bioinformatics 2008, 9(Suppl 11):S10.
15. Reeves RM, Ong FR, Matheny ME, Denny JC, Aronsky D, Gobbel GT, Montella D, Speroff T, Brown SH: Detecting temporal expressions in medical narratives. Int J Med Inform 2013, 82:118-127.
16. Goldin I, Chapman W: Learning to detect negation with "not"in medical texts. In Proc ACM-SIGIR 2003. 2003.
17. Agarwal S, Yu H: Biomedical negation scope detection with conditional random fields. J Am Med Inform Assoc 2010, 17:696-701.
18. Morante R, Daelemans W: A metalearning approach to processing the scope of negation. In Proc Thirteen Conf Comput Nat Lang Learn - CoNLL '09. Boulder, Colorado; 2009:21-29.
19. Vincze V, Szarvas G, Farkas R, Móra G, Csirik J: The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes. BMC Bioinformatics 2008, 9(Suppl 11):S9.
20. Cruz Díaz NP, Maña López MJ, Vázquez JM, Álvarez VP: A machine-learning approach to negation and speculation detection in clinical texts. J Am Soc Inf Sci Technol 2012, 63:1398-1410.
21. Light M, Qiu XY, Srinivasan P: The Language of Bioscience: Facts, Speculations, and Statements in Between. In HLTNAACL 2004 Work BioLINK 2004 Link Biol Lit Ontol Databases, Association for Computational Linguistics. Edited by Hirschman L, Pustejovsky J. 2004:17-24 [BIOLINK 2004 (Series editor)].
22. Velldal E: Predicting speculation: a simple disambiguation approach to hedge detection in biomedical literature. J Biomed Semant 2011, 2(Suppl 5):S7.
23. Goryachev S, Sordo M, Zeng Q, Ngo L: Implementation and Evaluation of Four Different Methods of Negation Detection. 2006.
24. Uzuner O, Zhang X, Sibanda T: Machine learning and rule-based approaches to assertion classification. J Am Med Inform Assoc 2011, 16:109-115.
25. Sun W, Rumshisky A, Uzuner O: Evaluating temporal relations in clinical text: 2012 i2b2 Challenge. J Am Med Inform Assoc 2013, 20:806-813.
26. Skeppstedt M: Negation detection in Swedish clinical text: an adaption of NegEx to Swedish. J Biomed Semant 2011, 2(Suppl 3):S3.
27. Deléger L, Grouin C: Detecting negation of medical problems in French clinical notes. In SIGHIT Symp Int Heal informatics - IHI'12. Edited by Proc ACM 2nd. New York, New York, USA: ACM Press; 2012:697-702.
28. ContextD: ConText for Dutch - Implementation and Resources. http://www.biosemantics.org/ContextD/.
29. Vlug A, van der Lei J, Mosseveld B, van Wijk M, van der Linden P, MC S, van Bemmel J: Postmarketing surveillance based on electronic patient records: the IPCI project. Methods Inf Med 1999, 38:339-344.
30. Adequate Dossiervorming Met Het Elektronisch Patiëntendossier (ADEPD): Nederlands Huisartsen Genootschap. 2013:20-21.
31. Bodenreider O: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res 2004, 32(suppl 1):D267-D270.
32. ConText implementation. http://code.google.com/p/negex/downloads/.
33. Google Translate. http://translate.google.com.
34. Apache OpenNLP library. http://opennlp.apache.org/.
35. Cohen J: A Coefficient of Agreement for Nominal Scales. Educ Psychol Meas 1960, 20:37-46 [Education and psychological measurement (Series editor)].
36. Altman DG: Some common problems in medical research. In Pract Stat Med Res, Volume Volume 1. London: Chapman & Hall; 1991:396-403.
37. Chapman WW, Hillert D, Velupillai S, Kvist M, Skeppstedt M, Chapman BE, Conway M, Tharp M, Mowery DL, Deleger L: Extending the NegEx Lexicon for Multiple Languages. In MEDINFO 2013 - Proc 14th World Congr Med Heal Informatics. Copenhagen, Denmark: Ios Press; 2013:677-681.
38. Apostolova E, Tomuro N, Demner-fushman D: Automatic Extraction of Lexico-Syntactic Patterns for Detection of Negation and Speculation Scopes. In Proc 49th Annu Meet Assoc Comput Linguist Hum Lang Technol short Pap - Vol 2. Portland, Oregon, USA: Association for Computational Linguistics; 2011:283-287.