Identifying important concepts from medical documents.
Automated medical concept recognition is important for medical informatics such as medical document retrieval and text mining research. In this paper, we present a software tool called keyphrase identification program (KIP) for identifying topical concepts from medical documents. KIP combines two functions: noun phrase extraction and keyphrase identification. The former automatically extracts noun phrases from medical literature as keyphrase candidates. The latter assigns weights to extracted noun phrases for a medical document based on how important they are to that document and how domain specific they are in the medical domain. The experimental results show that our noun phrase extractor is effective in identifying noun phrases from medical documents, so is the keyphrase extractor in identifying important medical conceptual terms. They both performed better than the systems they were compared to.
Li
Quanzhi
Q
Information Systems Department, New Jersey Institute of Technology, Newark, NJ 07102, USA. QL23@njit.edu <QL23@njit.edu>
Wu
Yi-Fang Brook
YF
D000465:Algorithms; D003210:Concept Formation; D006801:Humans; D016247:Information Storage and Retrieval; D007256:Information Systems; D007802:Language; D008037:Linguistics; D016347:Medical Records Systems, Computerized; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D009626:Terminology as Topic; D018875:Vocabulary, Controlled