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Evaluation of lexicon- and syntax-based negation detection algorithms using clinical text data

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Języki publikacji
Background: Clinical Text Analysis and Knowledge Extraction System (cTAKES) is an open-source natural language processing (NLP) system. In recent development modules of cTAKES, a negation detection (ND) algorithm is used to improve annotation capabilities and simplify automatic identification of negative context in large clinical documents. In this research, the two types of ND algorithms used are lexicon and syntax, which are analyzed using a database made openly available by the National Center for Biomedical Computing. The aim of this analysis is to find the pros and cons of these algorithms. Methods: Patient medical reports were collected from three institutions included the 2010 i2b2/VA Clinical NLP Challenge, which is the input data for this analysis. This database includes patient discharge summaries and progress notes. The patient data is fed into five ND algorithms: NegEx, ConText, pyConTextNLP, DEEPEN and Negation Resolution (NR). NegEx, ConText and pyCon- TextNLP are lexicon-based, whereas DEEPEN and NR are syntax-based. The results from these five ND algorithms are post-processed and compared with the annotated data. Finally, the performance of these ND algorithms is evaluated by computing standard measures including F-measure, kappa statistics and ROC, among others, as well as the execution time of each algorithm. Results: This research is tested through practical implementation based on the accuracy of each algorithm’s results and computational time to evaluate its performance in order to find a robust and reliable ND algorithm. Conclusions: The performance of the chosen ND algorithms is analyzed based on the results produced by this research approach. The time and accuracy of each algorithm are calculated and compared to suggest the best method.
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Bibliogr. 36 poz., rys., tab., wykr.
  • Research Scholar, Department of Computer Science, Bharathiar University, Coimbatore, India
  • Department of Computer Science, D. G. Vaishnav College, Chennai, India
  • 1. Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc 2012;18:544–51.
  • 2. Koopman B, Bruza P, Sitbon L, Lawley M. Analysis of the effect of negation on information retrieval of medical data. In: Proc 15th Australas Doc Comput Symp 2010:89–92.
  • 3. Scuba W, Tharp M, Mowery D, Tseytlin E, Liu Y, Drews FA, et al. Knowledge author: facilitating user-driven, domain content development to support clinical information extraction. J Biomed Semant 2016;7:42.
  • 4. Garla V, Re V Lo, Dorey-Stein Z, Kidwai F, Scotch M, Womack J, et al. The Yale cTAKES extensions for document classification: architecture and application. J Am Med Inform Assoc 2011;18:614–20.
  • 5. Mitchell KJ, Becich MJ, Berman JJ, Chapman WW, Gilbertson J, Gupta D, et al. Implementation and evaluation of a negation tagger in a pipeline-based system for information extraction from pathology reports. Stud Health Technol Inform 2004;107:663–7.
  • 6. Clark C, Aberdeen J, Coarr M, Tresner-kirsch D, Wellner B, Yeh A, et al. Determining assertion status for medical problems in clinical records. McLean, VA: Mitre Corporation, 2011:2–6.
  • 7. Ou Y, Patrick J. Automatic negation detection in narrative pathology reports. Artif Intell Med 2015;64:41–50.
  • 8. Jiang M, Chen Y, Liu M, Rosenbloom ST, Mani S, Denny JC, et al. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. J Am Med Inf Assoc 2011;18:601–6.
  • 9. Clark C, Aberdeen J, Coarr M, Tresner-Kirsch D, Wellner B, Yeh A, et al. MITRE system for clinical assertion status classification. J Am Med Inform Assoc 2011;18:563–7.
  • 10. Minard A-L, Ligozat A-L, Ben Abacha A, Bernhard D, Cartoni B, Deléger L, et al. Hybrid methods for improving information access in clinical documents: concept, assertion, and relation identification. J Am Med Inform Assoc 2011;18:588–93.
  • 11. Ballesteros M, Francisco V, Díaz A, Herrera J, Gervás P. Inferring the scope of negation in biomedical documents. Lect Notes Comput Sci 2012;7181 LNCS:363–75.
  • 12. Chapman WW, Dowling JN, Wagner MM. Fever detection from free-text clinical records for biosurveillance. J Biomed Inform 2004;37:120–7.
  • 13. Sanchez-Graillet O, Poesio M. Negation of protein-protein interactions: analysis and extraction. Bioinformatics 2007;23:424–32.
  • 14. Morante R. Descriptive analysis of negation cues in biomedical texts. Statistics 2009;1429–36.
  • 15. Horn LR. Natural history of negation. J Pragmat 1989;16: 269–80.
  • 16. 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;34:301–10.
  • 17. Aronow DB, Fangfang F, Croft WB. Ad hoc classification of radiology reports. J Am Med Inform Assoc 1999;6:393–411.
  • 18. 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.
  • 19. Gindl S, Kaiser K, Miksch S. Syntactical negation detection in clinical practice guidelines. Stud Health Technol Inform 2008;136:187–92.
  • 20. 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;42:839–51.
  • 21. Chapman BE, Lee S, Kang HP, Chapman WW. Document-level classification of CT pulmonary angiography reports based on an extension of the ConText algorithm. J Biomed Inform 2011;44:728–37.
  • 22. Mehrabi S, Krishnan A, Sohn S, Roch AM, Schmidt H, Kesterson J, et al. DEEPEN: a negation detection system for clinical text incorporating dependency relation into NegEx. J Biomed Inform 2015;54:213–9.
  • 23. Huang Y, Lowe H. A novel hybrid approach to automated negation detection in clinical radiology reports. J Am Med Inform 2007;304–11.
  • 24. Zhu Q, Li J, Wang H. A unified framework for scope learning via simplified shallow semantic parsing. In: EMNLP 2010 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing 2010:714–24.
  • 25. Sohn S, Wu S, Chute CG. Dependency parser-based negation detection in clinical narratives. AMIA Jt Summits Transl Sci Proc AMIA Summit Transl Sci 2012;2012:1–8.
  • 26. Gkotsis G, Velupillai S, Oellrich A, Dean H, Liakata M, Dutta R. Don’t let notes be misunderstood: a negation detection method for assessing risk of suicide in mental health records. In: Proc 3rd Work Comput Linguist Clin Psychol Linguist Signal Clin Real 2016:95–105.
  • 27. Lapponi E, Read J, Řvrelid L. Representing and resolving negation for sentiment analysis. In: Proc 12th IEEE Int Conf Data Min Work ICDMW 2012:687–92.
  • 28. Shivade C, de Marneffe MC, Fosler-Lussier E, Lai AM. Extending NegEx with kernel methods for negation detection in clinical text. In: Proc Work Extra-Propositional Asp Mean Comput Semant NAACL 2015:41–6.
  • 29. Kang T, Zhang S, Xu N, Wen D, Zhang X, Lei J. Detecting negation and scope in Chinese clinical notes using character and word embedding. Comput Methods Programs Biomed 2017;140:53–9.
  • 30. Goryachev S, Sordo M, Zeng QT, Ngo L. Implementation and evaluation of four different methods of negation detection. Boston, MA: DSG, 2006.
  • 31. Tanushi H, Dalianis H, Duneld M, Kvist M, Skeppstedt M, Velupillai S. Negation scope delimitation in clinical text using three approaches: NegEx, PyConTextNLP and SynNeg. In: Proc 19th Nord Conf Comput Linguist (NoDaLiDa 2013) 2013;1: 387–97.
  • 32. Wu S, Miller T, Masanz J, Coarr M, Halgrim S, Carrell D, et al. Negation’s not solved: generalizability versus optimizability in clinical natural language processing. PLoS One 2014;9:e112774.
  • 33. Uzuner O, South BR, Shen S, DuVall SL, Uzuner Ö, South BR, et al. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc 2011;18:552–6.
  • 34. Mowery DL, Chapman BE, Conway M, South BR, Madden E, Keyhani S, et al. Extracting a stroke phenotype risk factor from Veteran Health Administration clinical reports: an information content analysis. J Biomed Semantics 2016;7:26.
  • 35. Chapman BE, Mowery DL, Narasimhan E, Patel N, Chapman WW, Heilbrun ME. Assessing the feasibility of an automated suggestion system for communicating critical findings from chest radiology reports to referring physicians. In: Proc 15th Work Biomed Nat Lang Process 2016:181–5.
  • 36. Bruha I, Famili A. Postprocessing in machine learning and data mining. ACM SIGKDD Explor Newslett 2000;2:110–4.
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