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Extraction of Disease -Treatment in Short Texts

R. Arul Selvan, S. Ephina Thendral

Abstract


Machine Learning (ML) field of research is almost in many domains, and recently it becomes a powerful and reliable tool in the medical domain. ML is also being used for data analysis, such as detection of regularities from imperfect data. This project proposes a medical care application that is capable 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 in short text. We also focus on three relations: Cure, Prevent, and Side Effect of a particular disease. Automatic Information Mapping provides the ease way of finding the relevant data among a pool of data.

 


Keywords


Machine Learning, Semantics, Information Extraction, Knowledge Engineering, Data Mining

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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. Scho¨ 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.Asian Semantic Web Conf. (ISWC ’07/ ASWC ’07), pp. 523-536, 2007.


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