Open Access Open Access  Restricted Access Subscription or Fee Access

Kernel-Based Learning for Disease Recognition by Using Biomedical Relation Extraction

M. Syed Rabiya, G. Sundararaju

Abstract


The healthcare information system extracts the sentences from published medical papers that mention group of diseases and treatments, and identifies semantic relations that exist between diseases and treatments. The extracted information is less accurate. My proposed methodology obtains reliable outcomes that could be integrated in an application to be used in the medical care main. The potential value of my paper stands in the ML settings that I propose and in the fact that would outperform previous results on the same data set. The same data set to provide the fact.

Keywords


Healthcare, Machine Learning, Natural Language Processing.

Full Text:

PDF

References


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.

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.

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.

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. 171-178, 2006.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.