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    BIBLIOGRAFÍA RELACIONADA

  • (Aramaki et al. 2009) Aramaki E., Miura Y., Tonoike M., Ohkuma T., Mashuichi H., Ohe K.: TEXT2TABLE: Medical Text Summarization System Based on Named Entity Recognition and Modality Identification, Proceedings of the Human Language Technology conference and the North American chapter of the Association for Computational Linguistics, Workshop on BioNLP, pp.185-192, 2009.

  • (Averbuch et al., 2004) Averbuch M., Karson T.H., Ben-Ami B., Maimon O., Rokach L. Context-Sensitive Medical Information Retrieval. Studies in health technology and informatics, pp.282-286, 2004.

  • (Boytcheva et al., 2005) Boytcheva S., Strupchanska A., Paskaleva E., Tcharaktchiev D. Some Aspects of Negation Processing in Electronic Health Records. Proceedings of international workshop language and speech infrastructure for information access in the Balkan countries, 2005.

  • (Carrero et al.,, 2008) Carrero F.M., Cortizo J.C., Gómez J.M., de Buenaga M. In the development of a spanish metamap. 17th ACM conference on Information and knowledge management, pp. 1465-1466, 2008.

  • (Castro et al., 2010) Castro E., Iglesias A., Martínez P., Castaño L. Automatic Identification of biomedical Concepts in Spanish-Language Unstructured Clinical Texts. ACM International Health Informatics,751- 757, 2010.

  • (Chazard et al., 2011) Charzard E., Ficheur G., Bernonville S., Luyckx M., Beuscart R. Data Mining to Generate Adverse Drug Events Detection Rules. IEEE Transactions on Information Technology in Biomedicine, vol. 15, no 6, pp. 823-830, 2011.

  • (Chapman et al., 2001) Chapman W., Bridewell W., Hanbury P., Cooper G., Buchanan B. A simple algorithm for identifying negated findings and diseases in discharge summaries. J. Biomed, vol 34, no 5, 2001, 34(5);301-10.

  • (Chapman et al. 2013) Chapman W.W., Hilert D., Velupillai S., Kvist M., Skeppstedt M., Chapman B.E., Conway M., Tharp M., Mowery D.L, Deleger L. Extending the NegEx Lexicon for Multiple Languages. Proceedings of the 14th World Congress on Medical & Health Informatics, 2013.

  • (Chi et al.1983) Chi, EC., Sager N., Tick, LJ., Lyman, MS.. Relational data base modelling of free-text medical narrative. Med Inform, 8(3):209-23, 1983.

  • (Gurulingappa et al. 2012) Gurulingappa H., Fluck J., Hofmann-Apitius M., Toldo L. Idenfication of Adverse Drug Event Assertive Sentences in Medical Case Reports. Proceedings of Workshop on Knowledge Discovery in Health Care and Medicine, pp. 16-27, 2011.

  • (Friedman et al., 2004) Friedman C., Shagina L., Lussier Y ., Hripcsak G. 2004. Automated encoding of clinical documents based on natural language processing. JMIA, 11(5),pp.392?402, 2004.

  • (Huang et al., 2005) Huang, Y., Lowe, H., Klein, D. y Cucina, R. 2005. Improved Identification of Noun Phrases in Clinical Radiology Reports Using a High-Performance Statistical Natural Language Parser Augmented with the UMLS Specialist Lexicon. Journal of the American Medical Informatics Association (JAMIA),12:275 85.

  • (Huang and Lowe, 2007) Huang Y ., Lowe H.J. A Novel Hybrid Approach to Automated Negation Detection in Clinical Radiology Reports. J. Am Med. Assoc., 14(3), pp. 304-311, 2007.

  • (Karlsson et al., 2013) Karlsson I., Zhao J., Asker L., Boström H. Predicting Adverse Drug Events by Analyzing Electronic Patient Record. 13th Conference on Artificial Intelligence in Medicine, pp. 125-129, 2013.

  • (Ruiz et al., 2008) Ruiz B., García M., Aguirre U., Aguirre C. Factors predicting hospital readmissions related to adverse drug reactions. European Journal of Clinical Pharmacology, 64(7), pp. 715-722, 2008.

  • (Tsuruoka et al., 2005) Tsuruoka Y ., Tateishi Y ., Kim J., Ohta T., McNaught J., Ananiadou S., Tsujii J. Developing a Robust Part-of-Speech Tagger for Biomedical Text. Panhellenic Conference on Informatics, pp. 382-392, 2005.

  • (Taira et al., 2001), Taira RK., Soderland, SG. y Jakobovits, RM. Automatic structuring of radiology free- text reports. RadioGraphics,21:237 245, 2001.

  • (Wang et al., 2011) Wang X., Hripcsak G., Markatou M., Friedman C. Active Computerized Pharmacovigilance Using Natural Language, Statistics, and Electronic Health Records: a Feasibility Study. Journal of the American Medical Informatics Associatin, J Am Med Inform Asso,16, pp. 328-337, 2009.

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