Finding Semantic Relationship among Associated Medical Terms

Romania The literature of medicine and medicinal data is growing specialized and increasing by every day. Most of the data is contained by the journal of medicines and biology which makes this type of textual mining a central and core problem. Finding disease-medicine relationships requires laborious examination of hundreds of possible candidate heterogeneous factors. Most of the peoples face serious problems in extracting and finding useful information to access the clinical support from the currently available search engines and other tools, thus there should have some ability to identify the relationship between disease and other relevant factors which could support clinical diagnosis. In the paper, we are presenting a methodology for extracting useful information from Medline papers. The system tries to identify the relationship of an active disease and extract relevant medicine for the patient automatically. The core objective of proposed system is to find out or extracts only those documents that user is looking from a huge repository of documents or some other collection of facts and figures. The proposed method extracts some useful and interesting keywords that reveal sentences containing our desired task patterns. After finding the regularities of these patterns based on specific keywords, we apply the techniques of our system and mined semantic relationship. Our findings got more than 20 such keyword frequently produced relevant information. Proposed techniques give multiple medicines proposed by domain experts. The choice for proposed medicines are ranked on the basis of frequency and also based on the experience of different surveys performed by the authors in their papers.