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Transformation of Healthcare Information Using Machine Learning Approach

Dr.R. Gandhiraja, A. Merry Ida

Abstract


In recent year we develop Machine Learning (ML) approach. ML is to build computer systems that can adapt and learn from their experience.ML is the domain of research and recently it has develop in medical domain .The domain is automatically learning some task of healthcare information, medical management, patient health management etc.,. Healthcare deals with the resource, devices and method require optimizing storage, retrieval and use of information in health and bio medicine. Healthcare diagnosis, treatment and prevention of disease, illness, injury in human. This paper ML methodology is capable of identifying, spreading the information in healthcare. The extract sentence published from medical paper that mention on disease treatment. The result obtains reliable outcome integrated in medical domain

Keywords


Healthcare, Machine Learning, Natural Language Processing.

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References


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.

R. Bunescu, R. Mooney, Y. Weiss, B. School¨ lkopf, and J. Platt, ―Subsequence Kernels for Relation Extraction,‖ Advances in Neural Information Processing Systems, vol. 18, pp. 171-178, 2006.

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.

M. Craven, ―Learning to Extract Relations from Medline,‖ Proc. Assoc. for the Advancement of Artificial Intelligence, 1999.

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.

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.

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.

R. Gaizauskas, G. Demetriou, P.J. Artymiuk, and P. Willett, ―Protein Structures and Information Extraction from Biologicaltexts: The PASTA System,‖ Bioinformatics, vol. 19, no. 1, pp. 135-143, 2003.

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.

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.

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.

L. Hunter and K.B. Cohen, ―Biomedical Language Processing: What‘s beyond PubMed?‖ Molecular Cell, vol. 21-5, pp. 589-594,

L. Hunter, Z. Lu, J. Firby, W.A. Baumgartner Jr., 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.

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.

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.

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.

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.

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.

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.

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.

M. Ould Abdel Vetah, C. Ne´dellec, P. Bessie`res, F. Caropreso, A.-P. Manine, and S. Matwin, ―Sentence Categorization in Genomics Bibliography: A Naive Bayes Approach,‖ Actes de la Journe´e Informatique et Transcriptome, J.-F. Boulicaut and M. Gandrillon, eds., Mai 2003.

J. Pustejovsky, J. Capstan o, 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.


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