Paper Details

Named-entity recognition system (NER) [1] identifies different entities in many ways such name of personlocations, and organizations from news articles, reports, blogs, tweets. Main steps of named entity recognition areboundary detection of entities and classification of entities into already defined classes. This results of recognition andclassification is widely used in information retrieval and extraction.The main component of biomedical natural language processing is named entity recognition system which extractsinformation from the text and finally does the knowledge discovery. As amount of health and biomedical text beingavailable is huge and since much of the data is recorded in non-structured text, like in clinical notes and biomedicalpublications the bottleneck of biomedical information processing is how to make use of the knowledge resources andbuild scalable models to process large amounts of text. Biomedical named-entity recognition (BM-NER), also known asbiomedical concept identification or concept mapping, is a key step in biomedical language processing.In this paper, we are proposing a Biomedical Named Entity Recognition System for extracting Name, Problem and Testfrom the Textual Clinical Lab Reports using two widely accepted datasets, i2b2 [2] and GENIA corpora [3]and we areattempting to correlate the appropriate Treatment associated with it using Machine Learning Algorithms [4] andNatural Language Processing [5].Rest of the paper is organized as follows, section 2 gives in depth literature survey, in section 3 we discuss differentapproaches used for Bio-NER. In section 4 describes proposed system architecture.

Dr. K.S. Wagh, Aishwarya Kulkarni, Pratiksha Pawar, Neha Kirange, Shraddha Kashid, “Bio Medical Named Entity Recognition Using Machine Learning Algorithms”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 4, Issue 16, pp. -, January 2017.