Negation's not solved: Generalizability versus optimizability in clinical natural language processing

PLoS ONE

Volumn 9, Issue 11, 2014, Pages

  Wu, Stephen ^a,e   Miller, Timothy ^b   Masanz, James ^a   Coarr, Matt ^c   Halgrim, Scott ^d   Carrell, David ^d   Clark, Cheryl ^c  

^a MAYO CLINIC   (United States)

^b BOSTON CHILDREN S HOSPITAL   (United States)

^c MITRE CORPORATION   (United States)

^d GROUP HEALTH RESEARCH INSTITUTE   (United States)

^e OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; DATA ANALYSIS; ELECTRONIC MEDICAL RECORD; MACHINE LEARNING; NATURAL LANGUAGE PROCESSING; NEGATION; NOMENCLATURE; ALGORITHM; ARTIFICIAL INTELLIGENCE; BOOK; CLINICAL MEDICINE; CONTROLLED VOCABULARY; EDUCATION; HUMAN; SEMANTICS; STATISTICS AND NUMERICAL DATA;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLINICAL MEDICINE; HUMANS; NATURAL LANGUAGE PROCESSING; SEMANTICS; TEXTBOOKS AS TOPIC; VOCABULARY, CONTROLLED;

EID: 84911878838     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0112774     Document Type: Article

References (22)

1. Chapman W, Bridewell W, Hanbury P, Cooper G, Buchanan B (2001) A simple algorithm for identifying negated findings and diseases in discharge summaries. Journal of biomedical informatics 34: 301-310
2. Clark C, Aberdeen J, Coarr M, Tresner-Kirsch D, Wellner B, et al. (2011) MITRE system for clinical assertion status classification. J Am Med Inform Assoc 18: 563-567
3. Elkin PL, Brown SH, Bauer BA, Husser CS, Carruth W, et al. (2005) A controlled trial of automated classification of negation from clinical notes. BMC Med Inform Decis Mak 5: 13
4. Goldin I, ChapmanWW (2003) Learning to detect negation with 'not'in medical texts. Proc Workshop on Text Analysis and Search for Bioinformatics, ACM SIGIR.
5. Huang Y, Lowe HJ (2007) A novel hybrid approach to automated negation detection in clinical radiology reports. J Am Med Inform Assoc 14: 304-311
6. Mutalik PG, Deshpande A, Nadkarni PM (2001) Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS. J Am Med Inform Assoc 8: 598-609
7. Sohn S, Wu S, Chute CG (2012) Dependency Parser-based Negation Detection in Clinical Narratives. AMIA Summits on Translational Science proceedings AMIA Summit on Translational Science 2012: 1-8
8. Uzuner O, South BR, Shen S, DuVall SL (2011) 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc 18: 552-556
9. Daume III H (2007) Frustratingly easy domain adaptation. Proceedings of the Association for Computational Linguistics: 256-263
10. Morante R, Sporleder C (2012) Modality and Negation: An Introduction to the Special Issue. Computational Linguistics 38: 223-260
11. Chapman WW, Hillert D, Velupillai S, Kvist M, Skeppstedt M, et al. (2013) Extending the NegEx lexicon for multiple languages. Stud Health Technol Inform 192: 677-681
12. South BR, Phansalkar S, Swaminathan AD, Delisle S, Perl T, et al. (2007) Adaptation of the NegEx algorithm to Veterans Affairs electronic text notes for detection of influenza-like illness (ILI). AMIA Annu Symp Proc: 1118
13. Harkema H, Dowling JN, Thornblade T, Chapman WW (2009) ConText: An algorithm for determining negation, experiencer, and temporal status from clinical reports. Journal of biomedical informatics 42: 839-851
14. Garla V, Re VL, Dorey-Stein Z, Kidwai F, Scotch M, et al. (2011) The Yale cTAKES extensions for document classification: architecture and application. Journal of the American Medical Informatics Association 18: 614-620
15. Vincze V, Szarvas G, Farkas R, Mora G, Csirik J (2008) The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes. BMC Bioinformatics 9 Suppl 11: S9
16. Farkas R, Vincze V, Mora G, Csirik J, Szarvas G (2010) The CoNLL-2010 shared task: learning to detect hedges and their scope in natural language text. Proceedings of the Fourteenth Conference on Computational Natural Language Learning-Shared Task. Association for Computational Linguistics. 1-12
17. Zuccon G, Koopman B, Nguyen A, Vickers D, Butt L (2012) Exploiting medical hierarchies for concept-based information retrieval. Proceedings of the Seventeenth Australasian Document Computing Symposium. ACM. 111-114
18. Albright D, Lanfranchi A, Fredriksen A, Styler WF, Warner C, et al. (2013) Towards comprehensive syntactic and semantic annotations of the clinical narrative. Journal of the American Medical Informatics Association.
19. Cairns BL, Nielsen RD, Masanz JJ, Martin JH, Palmer MS, et al. (2011) The MiPACQ clinical question answering system. AMIA Annu Symp Proc 2011: 171-180
20. Ratnaparkhi A (1997) A Linear Observed Time Statistical Parser Based on Maximal Entropy Models. Proc EMNLP. 1-10
21. Pighin D, Moschitti A (2010) On reverse feature engineering of syntactic tree kernels. Proceedings of the Fourteenth Conference on Computational Natural Language Learning. Association for Computational Linguistics. 223-233
22. Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, et al. (2010) Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 17: 507-513