Classifying Drug Adverse Events using Social Media Data

Abstract

On time recognition of medication unfriendly occasions has been a basic issue for the pharmaceutical business, the conventional route was to recognize them at clinical preliminaries and after the medication arrives at the market gather the patient gripes from specialists. Be that as it may, this procedure devours time and has the danger of missing significant medication unfriendly responses. On time location of medication unfriendly occasions has been a basic issue for the pharmaceutical business, the customary path was to identify them at clinical preliminaries and after the medication arrives at the market gather the patient whines from specialists. In any case, this procedure expends time and has the danger of missing significant medication unfavorable responses. This research focuses on a Machine Learning approach to screening continuously from social media data such as twitter about drug adverse reactions and classified them from these contents.