SciELO - Scientific Electronic Library Online

 
 número43Are my Children Old Enough to Read these Books? Age Suitability AnalysisA Micro Artificial Immune System índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados

Artículo

Indicadores

Links relacionados

  • No hay artículos similaresSimilares en SciELO

Compartir


Polibits

versión On-line ISSN 1870-9044

Polibits  no.43 México ene./jun. 2011

 

Linguistically Motivated Negation Processing: An Application for the Detection of Risk Indicators in Unstructured Discharge Summaries

 

Caroline Hagege

 

XRCE – Xerox Research Centre Europe, 6 Chemin de Maupertuis, 38240 Meylan, France. (e–mail: Caroline.Hagege@xrce.xerox.com).

 

Manuscript received October 21, 2010.
Manuscript accepted for publication January 19, 2011.

 

Abstract

The paper proposes a linguistically motivated approach to deal with negation in the context of information extraction. This approach is used in a practical application: the automatic detection of cases of hospital acquired infections (HAI) by processing unstructured medical discharge summaries. One of the important processing steps is the extraction of specific terms expressing risk indicators that can lead to the conclusion of HAI cases. This term extraction has to be very accurate and negation has to be taken into account in order to really understand if a string corresponding to a potential risk indicator is attested positively or negatively in the document. We propose a linguistically motivated approach for dealing with negation using both syntactic and semantic information. This approach is first described and then evaluated in the context of our application in the medical domain. The results of evaluation are also compared with other related approaches dealing with negation in medical texts.

Key words: Negation detection, discharge summaries, dependency parsing.

 

DESCARGAR ARTÍCULO EN FORMATO PDF

 

REFERENCES

[1] S. Aït–Mokhtar, J.P. Chanod, and C. Roux, "Robustness beyond Shallowness: Incremental Deep Parsing," Natural Language Engineering, 8, pp.121–144, 2002.         [ Links ]

[2] P.L. Bikin, S.H. Brown, B.A. Bauer, C.S. Husser, W. Carruth, L.R Bergstrom, and D.L. Wahner–Roedler, "A controlled trial of automated classification of negation from clinical notes," BMC Medical Informatics and Decision Making, S:13, 2005.         [ Links ]

[3] W.W Chapman, W. Bridewell, P. Habury, G.F. Cooper, and B.G.Buchanan, "A simple algorithm for identifying negated findings and diseases in discharge summaries," Journal of Biomedical Informatics, 34, pp. 301–310, 2001.         [ Links ]

[4] S. Gindl, K. Kaiser, and S. Miksh, "Syntactical Negation Detection in Clinical Practice Guidelines," in Andersen, S.K.; Klein, G.O.; Schulz, S.; Aarts, J.; Mazzoleni, M.C. (eds.) eHealth Beyond the Horizon — Get IT There. Proc. of the 21st International Congress of the European Federation for Medical Informatics (MIE 2008), Göteborg, Sweden, IOS Press, 2008, pp. 187–192.         [ Links ]

[5] C. Hagège, P. Marchal, Q. Gicquel, S. Darmoni, S. Pereira, M–H. Metzger, "Linguistic and Temporal Processing for Discovering Hospital Acquired Infection from Patient Records," in Proceedings of the 2nd International Workshop on Knowledge Representation for Health Care (KR4HC–2010), Lisbon, Portugal, 2010.         [ Links ]

[6] H. Harkema, J. N. Dowling, T. Thomblade, and W. Chapman, "ConText: An Algorithm For Determining Negation, Experiencer, and Temporal Status from Clinical Reports," Journal of Biomedical Informatics, 42, pp. 839–851, 2009.         [ Links ]

[7] Y. Huang and H.J. Lowe, "A Novel Hybrid Approach to Automated Negation Detection in Clinical Radiology Reports," Journal of the American Medical Informatics Association (JAMIA), vol. 14(3), pp. 304–311, 2007.         [ Links ]

[8] Y. Krallinger, "Importance of negations and experimental qualifiers in biomedical literature," in Proceedings of the Workshop on Negation and Speculation in Natural Language Processing, Uppsala, Sweden, 2010, pp. 46–49.         [ Links ]

[9] R. Morante and W. Daelemans, "A metalearning approach to processing the scope of negation," in Proceedings of CoNLL 2009, 2009, pp. 21–29.         [ Links ]

[10] R. Morante, "Descriptive analysis of negation cues in biomedical texts," in Proceedings of the Seventh International Language Resources and Evaluation (LREC'10), Valletta, Malta, 2010, pp. 1429–1436.         [ Links ]

[11] P.G. Mutalik, A. Deshpande, and P.M. Nadkarni, "Use of General–purpose Negation Detection to Augment Concept Indexing of Medical Documents: A Quantitative Study Using the UMLS," Journal of the American Medical Informatics Association (JAMIA), vol. 8(6), pp. 598–609, 2001.         [ Links ]

[12] D. Proux, P. Marchal, F. Segond, I. Kergoulay, S. Darmoni, S. Pereira, Q. Gicquel, M–H. Metzger, "Natural Language Processing to detect Risk Patterns related to Hospital Acquired Infections," in Proceedings of RANLP 2009, Borovetz, Bulgaria, 2009, pp. 865–881.         [ Links ]

[13] V. Vincze, G. Szarvas, R. Farkas, G. Móra, and J. Csirik, "The Bioscope corpus: biomedical texts annotated for uncertainty, negation and their scopes," BMCBioinformatics, 9(Suppl 11):S9, 2008.         [ Links ]