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In this research, we provided a dictionary-based approach for identifying biomedical concepts from the literature. The approach first crawled experimental corpus by E-utilities and built a concept dictionary. Then, we developed an algorithm called Variable-step Window Identification Algorithm (VWIA) for matching biomedical concepts based on preprocessing, POS tagging and the formation of phrase block. The approach could identify embedded biomedical concepts and new concepts, which could identify concepts more completely. The proposed approach obtain 95.0% F-measure overall for the test dataset. Thus, it is promising for the method of biomedical text mining.

The primary biomedical literature is being generated at an unprecedented rate, and researchers cannot keep abreast of new developments in their fields. Biomedical natural language processing is being developed to address this issue, but building reliable systems often requires many expert-hours. We present an approach for automatically developing collections of regular expressions to drive high-performance concept recognition systems with minimal human interaction. We applied our approach to develop MutationFinder, a system for automatically extracting mentions of point mutations from the text. MutationFinder achieves performance equivalent to or better than manually developed mutation recognition systems, but the generation of its 759 patterns has required only 5.5 expert-hours. We also discuss the development and evaluation of our recently published high-quality, human-annotated gold standard corpus, which contains 1,515 complete point mutation mentions annotated in 813 abstracts. Both MutationFinder and the complete corpus are publicly available at .