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Template based Medical Reports Summarization

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Ahmed Y. Abu El-Qumsan, Alaa M. Elhalees

Ahmed Abu Y El-Qumsan and Alaa M Elhalees. Template based Medical Reports Summarization. International Journal of Computer Applications 179(17):47-55, February 2018. BibTeX

	author = {Ahmed Y. Abu El-Qumsan and Alaa M. Elhalees},
	title = {Template based Medical Reports Summarization},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2018},
	volume = {179},
	number = {17},
	month = {Feb},
	year = {2018},
	issn = {0975-8887},
	pages = {47-55},
	numpages = {9},
	url = {},
	doi = {10.5120/ijca2018916301},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The torrential information in the medical records is considered a great problem because it difficult to distinguish the needed and necessary information from the huge quantity of data. As a result, the importance of summarize medical reports is growing day after day. Medical information extraction is one of the important topics that aim to identify medical information and detect hidden relations. This topic is considered one of the most important topics in the field of text mining where is used to process unstructured texts and extract meaningful information which is hidden in the unstructured texts.

The information extracted from medical reports is very useful to medical staff to detect hidden relations between medical information, and making decisions that will improve the medical service for patients, in addition to saving time and effort.

In our paper, an approach that use template based medical reports summarization has been developed to transfer medical reports from semi structured and unstructured form to structured form. It classifies the identified entities then extracts important information such as diseases, medical procedures, and drugs. After that, it can discovery hidden relationship between medical information by using association rules. The dataset used in this paper was collected from the Palestinian Ministry of Health.

To evaluate the performance and effectiveness of our model, human expert has been used as a reference to measure the degree of acceptance of the extracted association rules which have been extracted from the dataset. So, Likert’s scale has been used for evaluation. After the data analysis obtained from the questionnaire. It shows us that the proportion of accuracy association rules, which have been extracted is about 80%.


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Template Based Summarization, Text Mining, Information Extraction, Medical Reports, Named Entity Recognition, Association Rules.