DRUG SUGGESTION CONCERNED AUTOMATED DRUG KNOWLEDGE ONTOLOGY CONSTRUCTION FRAMEWORK

Abstract

Drug effect identification suggesting proper drug is the most critical task in the medical care environment which needs to be done with more concern. In our previous research work, drug effect identification is performed from the real world tweets gathered from the twitter website using Relevancy and Similarity Aware Drug Comment Classification Framework (RSDCCF). However this method doesn’t focus on the faster response and proper drug suggestion based on side effects. This is focused in this research work by introducing the method namely Drug Suggestion Concerned Automated Drug Knowledge Ontology Construction Framework (DSCADKOCG). This research work can ensure the proper drug suggestion to the patients based on side effects. In this research work, automated ontology construction is performed based on drug tweets gathered from the social websites which can lead to construction of Drug Knowledge Source Ontology Construction. After ontology construction, drug learning is performed using the constructed ontology and the drug database using TSVM classifier. Based on this learned knowledge automated and fast drug suggestion is performed using Semantic query based drug suggestion approach. The overall evaluation of the research method is performed in the matlab simulation environment from which it can be proved that the proposed research technique can lead to provide the optimal outcome than the existing research techniques