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Issue No.06 - June (2011 vol.23)
pp: 801-814
Oana Frunza , University of Ottawa, Ottawa
Diana Inkpen , University of Ottawa, Ottawa
Thomas Tran , University of Ottawa, Ottawa
The Machine Learning (ML) field has gained its momentum in almost any domain of research and just recently has become a reliable tool in the medical domain. The empirical domain of automatic learning is used in tasks such as medical decision support, medical imaging, protein-protein interaction, extraction of medical knowledge, and for overall patient management care. ML is envisioned as a tool by which computer-based systems can be integrated in the healthcare field in order to get a better, more efficient medical care. This paper describes a ML-based methodology for building an application that is capable of identifying and disseminating healthcare information. It extracts sentences from published medical papers that mention diseases and treatments, and identifies semantic relations that exist between diseases and treatments. Our evaluation results for these tasks show that the proposed methodology obtains reliable outcomes that could be integrated in an application to be used in the medical care domain. The potential value of this paper stands in the ML settings that we propose and in the fact that we outperform previous results on the same data set.
Healthcare, machine learning, natural language processing.
Oana Frunza, Diana Inkpen, Thomas Tran, "A Machine Learning Approach for Identifying Disease-Treatment Relations in Short Texts", IEEE Transactions on Knowledge & Data Engineering, vol.23, no. 6, pp. 801-814, June 2011, doi:10.1109/TKDE.2010.152
[1] R. Bunescu and R. Mooney, "A Shortest Path Dependency Kernel for Relation Extraction," Proc. Conf. Human Language Technology and Empirical Methods in Natural Language Processing (HLT/EMNLP), pp. 724-731, 2005.
[2] R. Bunescu, R. Mooney, Y. Weiss, B. Schölkopf, and J. Platt, "Subsequence Kernels for Relation Extraction," Advances in Neural Information Processing Systems, vol. 18, pp. 171-178, 2006.
[3] 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. (TREC), 2004.
[4] M. Craven, "Learning to Extract Relations from Medline," Proc. Assoc. for the Advancement of Artificial Intelligence, 1999.
[5] I. 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] C. Giuliano, L. Alberto, and R. Lorenza, "Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature," Proc. 11th Conf. European Chapter of the Assoc. for Computational Linguistics, 2006.
[10] J. Ginsberg, H. Mohebbi Matthew, S.P. Rajan, B. Lynnette, S.S. Mark, and L. Brilliant, "Detecting Influenza Epidemics Using Search Engine Query Data," Nature, vol. 457, pp. 1012-1014, Feb. 2009.
[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.
[12] L. Hunter and K.B. Cohen, "Biomedical Language Processing: What's beyond PubMed?" Molecular Cell, vol. 21-5, pp. 589-594, 2006.
[13] L. Hunter, Z. Lu, J. Firby, W.A. BaumgartnerJr., H.L. Johnson, P.V. Ogren, and K.B. Cohen, "OpenDMAP: An Open Source, Ontology-Driven Concept Analysis Engine, with Applications to Capturing Knowledge Regarding Protein Transport, Protein Interactions and Cell-Type-Specific Gene Expression," BMC Bioinformatics, vol. 9, article no. 78, Jan. 2008.
[14] T.K. Jenssen, A. Laegreid, J. Komorowski, and E. Hovig, "A Literature Network of Human Genes for High-Throughput Analysis of Gene Expression," Nature Genetics, vol. 28, no. 1, pp. 21-28, 2001.
[15] R. Kohavi and F. Provost, "Glossary of Terms," Machine Learning, Editorial for the Special Issue on Applications of Machine Learning and the Knowledge Discovery Process, vol. 30, pp. 271-274, 1998.
[16] G. Leroy, H.C. Chen, and J.D. Martinez, "A Shallow Parser Based on Closed-Class Words to Capture Relations in Biomedical Text," J. Biomedical Informatics, vol. 36, no. 3, pp. 145-158, 2003.
[17] J. Li, Z. Zhang, X. Li, and H. Chen, "Kernel-Based Learning for Biomedical Relation Extraction," J. Am. Soc. Information Science and Technology, vol. 59, no. 5, pp. 756-769, 2008.
[18] T. Mitsumori, M. Murata, Y. Fukuda, K. Doi, and H. Doi, "Extracting Protein-Protein Interaction Information from Biomedical Text with SVM," IEICE Trans. Information and Systems, vol. E89D, no. 8, pp. 2464-2466, 2006.
[19] M. Yusuke, S. Kenji, S. Rune, M. Takuya, and T. Jun'ichi, "Evaluating Contributions of Natural Language Parsers to Protein-Protein Interaction Extraction," Bioinformatics, vol. 25, pp. 394-400, 2009.
[20] S. Novichkova, S. Egorov, and N. Daraselia, "MedScan, A Natural Language Processing Engine for MEDLINE Abstracts," Bioinformatics, vol. 19, no. 13, pp. 1699-1706, 2003.
[21] M. Ould Abdel Vetah, C. Nédellec, P. Bessières, F. Caropreso, A.-P. Manine, and S. Matwin, "Sentence Categorization in Genomics Bibliography: A Naive Bayes Approach," Actes de la Journée Informatique et Transcriptome, J.-F. Boulicaut and M. Gandrillon, eds., Mai 2003.
[22] J. Pustejovsky, J. Castaño, J. Zhang, M. Kotecki, and B. Cochran, "Robust Relational Parsing over Biomedical Literature: Extracting Inhibit Relations," Proc. Pacific Symp. Biocomputing, vol. 7, pp. 362-373, 2002.
[23] S. Ray and M. Craven, "Representing Sentence Structure in Hidden Markov Models for Information Extraction," Proc. Int'l Joint Conf. Artificial Intelligence (IJCAI '01), 2001.
[24] T.C. Rindflesch, L. Tanabe, J.N. Weinstein, and L. Hunter, "EDGAR: Extraction of Drugs, Genes, and Relations from the Biomedical Literature," Proc. Pacific Symp. Biocomputing, vol. 5, pp. 514-525, 2000.
[25] B. Rosario and M.A. Hearst, "Semantic Relations in Bioscience Text," Proc. 42nd Ann. Meeting on Assoc. for Computational Linguistics, vol. 430, 2004.
[26] P. Srinivasan and T. Rindflesch, "Exploring Text Mining from Medline," Proc. Am. Medical Informatics Assoc. (AMIA) Symp., 2002.
[27] 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.
[28] J. Thomas, D. Milward, C. Ouzounis, S. Pulman, and M. Carroll, "Automatic Extraction of Protein Interations from Scientific Abstracts," Proc. Pacific Symp. Biocomputing, vol. 5, pp. 538-549, 2000.
[29] A. Yakushiji, Y. Tateisi, Y. Miyao, and J. Tsujii, "Event Extraction from Biomedical Papers Using a Full Parser," Proc. Pacific Symp. Biocomputing, vol. 6, pp. 408-419, 2001.
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