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Articles by Eslam Amer
Total Records ( 2 ) for Eslam Amer
  Mohamed Ab-d-ElFattah and Eslam Amer
  Quality management system implementation has become a must for institutions in Arab countries to be able to enter tenders. One of the most common quality standards is the NCAAA quality management standard and many institutions seek NCAAA standards accreditation in today’s highly competitive market. However, in getting this accreditation, most institutions face difficulties such as the huge amount of paperwork, improper documentation, poor communication among employees and low employee morale as a consequence of lack of motivation. The study presents a higher education Quality Decision Support System (QDSS) that integrates the quality tools as well as the process quality information. In a development type of research, researchers must identify the constraints imposed by the environment, state the objectives of the development effects (i.e., the focus of the research) and define the functionalities of the resulting system to achieve the stated objectives. The results shows that the application of QDSS can optimize the process of design academic program, shorten the cycle time of quality, reduce the cost and realize quality improvement continuously.
  Eslam Amer and Mohammed Abel Elfatah
  Due to the high growth rate in claiming disability benefits, Social Security Administration (SSA) faces a real overload challenge. Disability determination process has turned out to be time-consuming, complicated and expensive. By unlocking patient’s details, we can gain valuable information that could lead to improvement in the quality of healthcare, reducing time and healthcare cost. This study presents an approach to ease the process of disability determination. Our approach uses natural language processing and biomedical text mining to deal with data stored in patient’s Electronic Healthcare Records (EHRs). Such data may encode significant information about the patient’s case. The developed system extracts relevant medical entities and builds relations between symptoms and other clinical signature modifiers. The proposed system uses extracted information as evaluation features. Such features decide whether an applicant should gain disability benefits. Evaluations show that the proposed system accurately extracts symptoms and other laboratory marks with high F-measures (93.5-95.6%). The proposed automated system deduces right assessments to approve or reject the applicants for disability benefits.
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