Hanan Elazhary

Work place: Computer Science Department, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

E-mail: helazhary@kau.edu.sa


Research Interests: Computational Science and Engineering, Computer systems and computational processes, Data Structures and Algorithms


Hanan Elazhary earned her B.Sc. and M.Sc. degrees from the Department of Electronics and Communications Engineering, Cairo University. She earned her Ph.D. degree in Computer Science and Engineering from the University of Connecticut, USA. Currently, she is an associate professor in the Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia and the Computers and Systems Department, Electronics Research Institute, Cairo, Egypt.

Author Articles
NegMiner: An Automated Tool for Mining Negations from Electronic Narrative Medical Documents

By Hanan Elazhary

DOI: https://doi.org/10.5815/ijisa.2017.04.02, Pub. Date: 8 Apr. 2017

Mining negations from electronic narrative medical documents is one of the prominent data mining applications. Since medical documents are freely written, it is impossible to consider all possible sentence structures in advance and so frequent update of mining algorithms is inevitable. Unfortunately most of the proposed algorithms in the literature are too complex to be easily updated. Besides, most of them cannot be easily ported to other natural languages. The simple NegEx algorithm utilizes only two regular expressions and sets of terms to mine negations from narrative medical documents and so does not suffer from these shortcomings. Meanwhile, it has shown impressive mining results and so it is the most widely adopted algorithm. This paper proposes the Negation Mining (NegMiner) tool to address some of the shortcomings of the NegEx algorithm. The NegMiner exploits some basic syntactic and semantic information to deal with contiguous and multiple negations. It is a user-friendly tool that facilitates the task of knowledge base update and the task of document analysis through the use of PDF files. This also makes it able to deal with the existence of a medical finding several times in a single sentence. Experimental results have shown the superiority of the mining results of the NegMiner in comparison to the simulated NegEx algorithm.

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