期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2015
卷号：4
期号：6
页码：12374-12378
出版社：IJECS
摘要：The Machine Learning (ML) field has gained its momentum in almost any domain of research and just recentlyhas become a reliable tool in the medical domain. Extracting medical relations is very trivial task since the medicalinformation is stored in textual format and the database of medical information is also very large in size for example Medlineis the medical database that contains 21 million records from 5000 selected publications. In addition to that web pagecontaining medical information also contains some unrelated contents like advertisements, scroll bars, quick links, relatedsearches etc., manually extracting only relevant information from such a huge database is very difficult task. To reduce useroverhead of extracting useful information current approach is proposed.This approach presents the efficient machine learning algorithm and techniques used in extracting disease symptomand treatment related sentences from Medline. In this approach Multinomial Naive Bayes algorithm and several othertechniques are used to extract semantic relation between disease symptom and their associated treatment. The proposed systemgives the user exactly the Disease Symptom and Treatment related sentences by avoiding unnecessary information and thistechnique can be integrated with any medical management system to make better medical decisions
