<strong>ملخص الدراسة:</strong>

The torrential information in the medical records is considered_x000D__x000D_
a great problem because it difficult to distinguish the needed_x000D__x000D_
and necessary information from the huge quantity of data. As_x000D__x000D_
a result, the importance of summarize medical reports is_x000D__x000D_
growing day after day. Medical information extraction is one_x000D__x000D_
of the important topics that aim to identify medical_x000D__x000D_
information and detect hidden relations. This topic is_x000D__x000D_
considered one of the most important topics in the field of text_x000D__x000D_
mining where is used to process unstructured texts and extract_x000D__x000D_
meaningful information which is hidden in the unstructured_x000D__x000D_
texts._x000D__x000D_
The information extracted from medical reports is very useful_x000D__x000D_
to medical staff to detect hidden relations between medical_x000D__x000D_
information, and making decisions that will improve the_x000D__x000D_
medical service for patients, in addition to saving time and_x000D__x000D_
effort._x000D__x000D_
In our paper, an approach that use template based medical_x000D__x000D_
reports summarization has been developed to transfer medical_x000D__x000D_
reports from semi structured and unstructured form to_x000D__x000D_
structured form. It classifies the identified entities then_x000D__x000D_
extracts important information such as diseases, medical_x000D__x000D_
procedures, and drugs. After that, it can discovery hidden_x000D__x000D_
relationship between medical information by using association_x000D__x000D_
rules. The dataset used in this paper was collected from the_x000D__x000D_
Palestinian Ministry of Health._x000D__x000D_
To evaluate the performance and effectiveness of our model,_x000D__x000D_
human expert has been used as a reference to measure the_x000D__x000D_
degree of acceptance of the extracted association rules which_x000D__x000D_
have been extracted from the dataset. So, Likert’s scale has_x000D__x000D_
been used for evaluation. After the data analysis obtained_x000D__x000D_
from the questionnaire. It shows us that the proportion of_x000D__x000D_
80% accuracy association rules, which have been extracted is about

<strong>توثيق المرجعي (APA)</strong>

ملخص الدراسة:

The torrential information in the medical records is considered_x000D__x000D_
a great problem because it difficult to distinguish the needed_x000D__x000D_
and necessary information from the huge quantity of data. As_x000D__x000D_
a result, the importance of summarize medical reports is_x000D__x000D_
growing day after day. Medical information extraction is one_x000D__x000D_
of the important topics that aim to identify medical_x000D__x000D_
information and detect hidden relations. This topic is_x000D__x000D_
considered one of the most important topics in the field of text_x000D__x000D_
mining where is used to process unstructured texts and extract_x000D__x000D_
meaningful information which is hidden in the unstructured_x000D__x000D_
texts._x000D__x000D_
The information extracted from medical reports is very useful_x000D__x000D_
to medical staff to detect hidden relations between medical_x000D__x000D_
information, and making decisions that will improve the_x000D__x000D_
medical service for patients, in addition to saving time and_x000D__x000D_
effort._x000D__x000D_
In our paper, an approach that use template based medical_x000D__x000D_
reports summarization has been developed to transfer medical_x000D__x000D_
reports from semi structured and unstructured form to_x000D__x000D_
structured form. It classifies the identified entities then_x000D__x000D_
extracts important information such as diseases, medical_x000D__x000D_
procedures, and drugs. After that, it can discovery hidden_x000D__x000D_
relationship between medical information by using association_x000D__x000D_
rules. The dataset used in this paper was collected from the_x000D__x000D_
Palestinian Ministry of Health._x000D__x000D_
To evaluate the performance and effectiveness of our model,_x000D__x000D_
human expert has been used as a reference to measure the_x000D__x000D_
degree of acceptance of the extracted association rules which_x000D__x000D_
have been extracted from the dataset. So, Likert’s scale has_x000D__x000D_
been used for evaluation. After the data analysis obtained_x000D__x000D_
from the questionnaire. It shows us that the proportion of_x000D__x000D_
80% accuracy association rules, which have been extracted is about

توثيق المرجعي (APA)