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Reseach Article

Extraction of Semantic Biomedical Relations from Medline Abstracts using Machine Learning Approach

Published on May 2012 by Suchitra A, Sudha R
National Conference on Advances in Computer Science and Applications (NCACSA 2012)
Foundation of Computer Science USA
NCACSA - Number 4
May 2012
Authors: Suchitra A, Sudha R
6b1d882d-0c23-424b-91fa-855051937679

Suchitra A, Sudha R . Extraction of Semantic Biomedical Relations from Medline Abstracts using Machine Learning Approach. National Conference on Advances in Computer Science and Applications (NCACSA 2012). NCACSA, 4 (May 2012), 1-4.

@article{
author = { Suchitra A, Sudha R },
title = { Extraction of Semantic Biomedical Relations from Medline Abstracts using Machine Learning Approach },
journal = { National Conference on Advances in Computer Science and Applications (NCACSA 2012) },
issue_date = { May 2012 },
volume = { NCACSA },
number = { 4 },
month = { May },
year = { 2012 },
issn = 0975-8887,
pages = { 1-4 },
numpages = 4,
url = { /proceedings/ncacsa/number4/6497-1022/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advances in Computer Science and Applications (NCACSA 2012)
%A Suchitra A
%A Sudha R
%T Extraction of Semantic Biomedical Relations from Medline Abstracts using Machine Learning Approach
%J National Conference on Advances in Computer Science and Applications (NCACSA 2012)
%@ 0975-8887
%V NCACSA
%N 4
%P 1-4
%D 2012
%I International Journal of Computer Applications
Abstract

Machine Learning (ML) is a natural outgrowth of the intersection of Computer Science and Statistics. Machine Learning has now become a reliable tool in the medical domain. ML which act as a tool by which computer-based systems could be integrated in the healthcare field in order to get a better and efficient health care. This methodology for building an application is capable of identifying and extracting healthcare information. The proposed system focuses on two main tasks. The first task identifies the sentences which are published in Medline abstracts. This task is similar to the task of sentence scanning contained in the medical abstract of an article in order to present to the user-only sentences that are identified as containing relevant information. The second task has a deeper semantic dimension and it focus on identifying semantic relations exists between disease-treatment. It focuses on three relations: Cure, Prevent, Side Effect and also focuses a subset of the eight relations that the corpus is annotated with. The proposed methodology obtains reliable outcomes and that could be integrated in an application to be used in the medical care domain. The framework's capabilities can be used in a commercial recommender system and it is integrated in a new Electronic Health Record system.

References
  1. R. Bunescu and R. Mooney, "A Shortest Path Dependency Kernel for Relation Extraction," Proc. Conf. Human Language Technology and Empirical Methods in EMNLP), pp. 724-731, 2005.
  2. A. M. Cohen and W. R. Hersh, and R. T. Bhupatiraju, "Feature Generation, Feature Selection, Classifiers, and Conceptual Drift for Biomedical Document Triage," Proc. 13th Text Retrieval Conf. 2004.
  3. R. Bunescu, R. Mooney, Y. Weiss, B. Scho¨ lkopf, and J. Platt, "Subsequence Kernels for Relation Extraction," Advances in Neural Information Processing Systems, vol. 18, pp. pp. 171-178, 2006.
  4. M. Craven, "Learning to Extract Relations from Medline," Proc. Assoc. for the Advancement of Artificial Intelligence, 1999.
  5. Donaldson et al. , "PreBIND and Textomy: Mining the Biomedical Literature for Protein-Protein Interactions Using a Support Vector Machine," BMC Bioinformatics, vol. 4, 2003.
  6. C. Friedman, P. Kra, H. Yu, M. Krauthammer, and A. Rzhetsky, "GENIES: A Natural Language Processing System for the Extraction of Molecular Pathways from Journal Articles," Bioinformatics, vol. 17, pp. S74-S82, 2001.
  7. O. Frunza and D. Inkpen, "Textual Information in Predicting Functional Properties of the Genes," Proc. Workshop Current Trends in Biomedical Natural Language Processing (BioNLP) in conjunction with Assoc. for Computational Linguistics (ACL '08), 2008.
  8. R. Gaizauskas, G. Demetriou, P. J. Artymiuk, and P. Willett, "Protein Structures and Information Extraction from Biological Texts: The PASTA System," Bioinformatics, vol. 19, no. 1, pp. 135-143, 2003.
  9. B. J. Stapley and G. Benoit, "Bibliometrics: Information Retrieval Visualization from Co-Occurrences of Gene Names in MEDLINE Abstracts," Proc. Pacific Symp. Biocomputing, vol. 5, pp. 526-537, 2000.
  10. B. Rosario and M. A. Hearst, "Semantic Relations in Bioscience Text," Proc. 42nd Ann. Meeting on Assoc. for computational Linguistics, vol. 430, 2004.
  11. M. Goadrich, L. Oliphant, and J. Shavlik, "Learning Ensembles of First-Order Clauses for Recall-Precision Curves: A Case Study in Biomedical Information Extraction," Proc. 14th Int'l Conf. Inductive Logic Programming, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Healthcare Machine Learning Natural Language Processing