Hideyuki Tanushi
Dept. Of Computer and Systems Sciences (DSV), Stockholm University, Kista, Sweden
Hercules Dalianis
Dept. of Computer and Systems Sciences (DSV), Stockholm University, Kista, Sweden
Martin Duneld
Dept. of Computer and Systems Sciences (DSV), Stockholm University, Kista, Sweden
Maria Kvist
Dept. of Computer and Systems Sciences (DSV), Stockholm University, Kista, Sweden and Dept. of clinical immunology and transfusion medicine, Karolinska University Hospital, Stockholm, Sweden
Maria Skeppstedt
Dept. of Computer and Systems Sciences (DSV), Stockholm University, Kista, Sweden
Sumithra Velupillai
Dept. of Computer and Systems Sciences (DSV), Stockholm University, Kista, Sweden
Ladda ner artikel
Ingår i: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013); May 22-24; 2013; Oslo University; Norway. NEALT Proceedings Series 16
Linköping Electronic Conference Proceedings 93:35, s. 387-397
NEALT Proceedings Series 16:35, p. 387-397
Publicerad: 2013-05-17
ISBN: 978-91-7519-589-6
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
Negation detection is a key component in clinical information extraction systems; as health record text contains reasonings in which the physician excludes different diagnoses by negating them. Many systems for negation detection rely on negation cues (e.g. not); but only few studies have investigated if the syntactic structure of the sentences can be used for determining the scope of these cues. We have in this paper compared three different systems for negation detection in Swedish clinical text (NegEx; PyConTextNLP and SynNeg); which have different approaches for determining the scope of negation cues. NegEx uses the distance between the cue and the disease; PyConTextNLP relies on a list of conjunctions limiting the scope of a cue; and in SynNeg the boundaries of the sentence units; provided by a syntactic parser; limit the scope of the cues. The three systems produced similar results; detecting negation with an F-score of around 80%; but using a parser had advantages when handling longer; complex sentences or short sentences with contradictory statements.
Aronow; D. B.; Fangfang; F.; and Croft; W. B. (1999). Ad Hoc Classification of Radiology Reports. J Am Med Inform Assoc; 5(6):393–411.
Ballesteros; M.; Francisco; V.; Díaz; A.; Herrera; J.; and Gervás; P. (2012). Inferring the scope of negation in biomedical documents. In 13th International Conference on Intelligent Text Processing and Computational Linguistics (CICLING 2012); New Delhi. Springer; Springer.
Chapman; B.; Lee; S.; Kang; H.; and Chapman; W. (2011). Document-level classification of CT pulmonary angiography reports based on an extension of the ConText algorithm. J Biomed Inform; 44(5):728–737.
Chapman; W.; Bridewell; W.; Hanbury; P.; Cooper; G.; and Buchanan; B. (2001a). Evaluation of negation phrases in narrative clinical reports. In Proceedings AMIA; pages 105–109.
Chapman; W. W.; Bridewell; W.; Hanbury; P.; Cooper; G. F.; and Buchanan; B. G. (2001b). A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform; pages 34–301.
Elkin; P. L.; Brown; S. H.; Bauer; B. A.; Husser; C. S.; Carruth; W.; Bergstrom; L. R.; ; and Wahner-Roedler; D. L. (2005). A controlled trial of automated classification of negation from clinical notes. BMC Med Inform Decis Mak; 13(5).
Goryachev; A.; Sordo; M.; Ngo; L.; and Zeng; Q. (2006). Implementation and evaluation of four different methods of negation detection. Technical report; DSG.
Harkema; H.; Dowling; J. N.; Thornblade; T.; and Chapman; W. W. (2009). Context: An algorithm for determining negation; experiencer; and temporal status from clinical reports. J. of Biomedical Informatics; 42(5):839–851.
Hassel; M.; Henriksson; A.; and Velupillai; S. (2011). Something Old; Something New – Applying a Pre-trained Parsing Model to Clinical Swedish. In Proc. 18th Nordic Conf. on Comp. Ling. - NODALIDA ’11.
Huang; Y. and Lowe; H. J. (2007). A Novel Hybrid Approach to Automated Negation Detection in Clinical Radiology Reports. JAMIA; 3(14):304–311.
Knutsson; O.; Bigert; J.; and Kann; V. (2003). A robust shallow parser for Swedish. In Proc. 14th Nordic Conf. on Comp. Ling. – NODALIDA ’03.
Morante; R. and Blanco; E. (2012). *sem 2012 shared task: resolving the scope and focus of negation. In Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task; and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation; SemEval ’12; pages 265–274; Stroudsburg; PA; USA. Association for Computational Linguistics.
Morante; R. and Daelemans; W. (2009). Learning the scope of hedge cues in biomedical texts. In Proceedings of the Workshop on BioNLP; pages 28–36.
Mutalik; P.; Deshpande; A.; and Nadkarni; P. (2001). Use of general-purpose negation detection to augment concept indexing of medical documents a quantitative study using the umls. JAMIA; 8(6):598–609.
Nivre; J.; Hall; J.; Nilsson; J.; Chanev; A.; Eryigit; G.; Kübler; S.; Marinov; S.; and Marsi; E. (2007). MaltParser: A language-independent system for data-driven dependency parsing. Natural Language Engineering; 13(2):95–135.
Rokach; L.; Romano; R.; and Maimon; O. (2010). Negation recognition in medical narrative reports. Information Retrieval; 11(6):499–538.
Skeppstedt; M. (2011). Negation detection in Swedish clinical text: An adaption of NegEx to Swedish. Journal of Biomedical Semantics; 2(Suppl 3):S3.
Velldal; E.; Øvrelid; L.; Read; J.; and Oepen; S. (2012). Speculation and negation: Rules; rankers; and the role of syntax. Comput. Linguist.; 38(2):369–410.
Velupillai; S.; Dalianis; H.; and Kvist; M. (2011). Factuality Levels of Diagnoses in Swedish Clinical Text. In Moen; A.; Andersen; S. K.; Aarts; J.; and Hurlen; P.; editors; Proc. XXIII International Conference of the European Federation for Medical Informatics – User Centred Networked Health Care; pages 559 – 563; Oslo. IOS Press.
Velupillai; S.; Skeppstedt; M.; Kvist; M.; Mowery; D.; Chapman; B.; Dalianis; H.; and Chapman; W. (2013). Porting a rule-based assertion classifier for clinical text from English to Swedish. In Proc. 4th Louhi 2013; Sydney; Australia.
Zhu; Q.; Li; J.; Wang; H.; and Zhou; G. (2010). A unified framework for scope learning via simplified shallow semantic parsing. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing; EMNLP ’10; pages 714–724; Stroudsburg; PA; USA. Association for Computational Linguistics.
