Abstract
This paper addresses the problem of extracting and processing relevant information from unstructured electronic documents of the biomedical domain. The documents are full scientific papers. This problem imposes several challenges, such as identifying text passages that contain relevant information, collecting the relevant information pieces, populating a database and a data warehouse, and mining these data. For this purpose, this paper proposes the IEDSS-Bio, an environment for Information Extraction and Decision Support System in Biomedical domain. In a case study, experiments with machine learning for identifying relevant text passages (disease and treatment effects, and patients number information on Sickle Cell Anemia papers) showed that the best results (95.9% accuracy) were obtained with a statistical method and the use of preprocessing techniques to resample the examples and to eliminate noise.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Feldman, R., Sanger, J.: The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, New York (2007)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery in Databases. AI Magazine 17(3), 37–54 (1996)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)
Džeroski, S.: Multi-Relational Data Mining: An Introduction. ACM SIGKDD Explorations Newsletter 5(1), 1–16 (2003)
Cohen, K.B., Hunter, L.: Getting Started in Text Mining. PLoS Computational Biology 4(1), 1–3 (2008)
Krauthammer, M., Nenadic, G.: Term Identification in the Biomedical Literature. Journal of Biomedical Informatics 37(6), 512–526 (2004)
Ananiadou, S., McNaught, J. (eds.): Text Mining for Biology and Biomedicine. Artech House, Norwood (2006)
Tsuruoka, Y., Tsujii, J.: Improving the Performance of Dictionary-Based Approaches in Protein Name Recognition. Journal of Biomedical Informatics 37(6), 461–470 (2004)
Chun, H.-W., Tsuruoka, Y., Kim, J.-D., Shiba, R., Nagata, N., Hishiki, T., Tsujii, J.: Extraction of Gene-Disease Relations from Medline Using Domain Dictionaries and Machine Learning. In: 11th PSB, Hawaii, pp. 4–15 (2006)
Mika, S., Rost, B.: NLProt: Extracting Protein Names and Sequences from Papers. Nucleic Acids Research 32(suppl. 2), 634–637 (2004)
Seki, K., Mostafa, J.: A Hybrid Approach to Protein Name Identification in Biomedical Texts. Information Processing & Management 41(4), 723–743 (2005)
Hanisch, D., Fundel, K., Mevissen, H., Zimmer, R., Fluck, J.: Prominer: Rule-Based Protein and Gene Entity Recognition. BMC Bioinf. 6(suppl. 1), S14 (2005)
Tanabe, L., Wilbur, W.J.: Tagging Gene and Protein Names in Biomedical Text. Bioinformatics 18(8), 1124–1132 (2002)
Bremer, E.G., Natarajan, J., Zhang, Y., DeSesa, C., Hack, C.J., Dubitzky, W.: Text Mining of Full Text Articles and Creation of a Knowledge Base for Analysis of Microarray Data. In: López, J.A., Benfenati, E., Dubitzky, W. (eds.) KELSI 2004. LNCS (LNAI), vol. 3303, pp. 84–95. Springer, Heidelberg (2004)
Garten, Y., Altman, R.: Pharmspresso: A Text Mining Tool for Extraction of Pharmacogenomic Concepts and Relationships from Full Text. BMC Bioinf. 10(suppl. 2), S6 (2009)
Tanabe, L., Wilbur, W.J.: Tagging Gene and Protein Names in Full Text Articles. In: Workshop on NLP in the Biomedical Domain, pp. 9–13. ACL, Phildadelphia (2002)
Cohen, A.M., Hersh, W.R.: A Survey of Current Work in Biomedical Text Mining. Briefings in Bioinformatics 6(1), 57–71 (2005)
Pinto, A.C.S., Matos, P.F., Perlin, C.B., Andrade, C.G., Carosia, A.E.O., Lombardi, L.O., Ciferri, R.R., Pardo, T.A.S., Ciferri, C.D.A., Vieira, M.T.P.: Technical Report Sickle Cell Anemia. Technical Report, Federal University of São Carlos (2009), http://sca.dc.ufscar.br/download/files/report.sca.pdf
Fleiss, J.L.: Measuring Nominal Scale Agreement among Many Raters. Psychological Bulletin 76(5), 378–382 (1971)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Anthony, L., Lashkia, G.V.: Mover: A Machine Learning Tool to Assist in the Reading and Writing of Technical Papers. IEEE Trans. Prof. Comm. 46(3), 185–193 (2003)
Landis, J.R., Koch, G.G.: The Measurement of Observer Agreement for Categorical Data. Biometrics 33(1), 159–174 (1977)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Matos, P.F., Lombardi, L.O., Pardo, T.A.S., Ciferri, C.D.A., Vieira, M.T.P., Ciferri, R.R. (2010). An Environment for Data Analysis in Biomedical Domain: Information Extraction for Decision Support Systems. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13022-9_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-13022-9_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13021-2
Online ISBN: 978-3-642-13022-9
eBook Packages: Computer ScienceComputer Science (R0)