String Kernel Approach for Efficient Extraction of Medical Relations

Abstract

This paper presents a methodology for building an application that can classify healthcare information. It extracts informative sentences from medical papers that mentions about diseases and treatments, and then recognizes semantic relations that exist between the entities in the informative sentences. Support Vector Machine algorithm with String Kernel is used for relation identification. The proposed system avoids unnecessary information and gives the user disease and Treatment related sentences from medical pages. Evaluation results for this approach show that the proposed methodology obtains reliable results. The string kernel approach is also compared with naïve bayes approach and it was found that string kernel approach outperforms naïve bayes method for classification. This technique can be integrated with any medical management system to make good decisions and in patient management system by automatically mining the biomedical information from digital repositories. This system enables easy access to medical information in rural areas where there is a relative shortage of physicians. General TermsClassification, Machine learning.