Information Extraction of Diseases and its Application

Named Entity Recognition is an essential module of Information Extraction in the field of bio-medical and diseases are one of the most important sector to study in the medical field, but since the amount of incessantly updated information on diseases is huge and is merely accessible in the form of published journals or articles. An efficient Named Entity Recognizer is needed to extract diseases directly from the input given in the form of articles and to annotate the extracted terms with the knowledge base. The Named Entity Recognizer techniques must first identify the targeted terms. Though biomedical articles often consist of proper nouns recently prepared by the authors, and dictionaries which are conventional methods based on domain specific cannot identify such unidentified words. This study will identify a better and efficient Information extraction system which will extract diseases from the given biomedical text using techniques such as dictionary based and machine learning based (K-nearest neighbor and Naïve Bayes) techniques. The efficiency of both techniques and both algorithms have been measured through confusion matrix and machine learning approach more specifically K-nearest neighbor has been found more proficient as compared to other techniques.