Design of an Interactive Biomedical Text Mining Framework to Recognize Real-Time Drug Entities Using Machine Learning Algorithms

Abstract

Drug Named Entity Recognition (DNER) is the process of identifying drug names from unstructured biomedical text. Most of the research works in this area make use of the available benchmark biomedical corpus to train and test the DNER system. In this paper, we propose an interactive biomedical text mining framework to access the scientific abstracts online, to pre-process and to extract drug entity through state – of – art machine learning techniques. This framework facilitates recognizing new drug names from real time biomedical abstracts from PubMed as well as from existing benchmark test datasets.