Decision support systems improve medical diagnosis and minimize diagnostic errors. Existing diagnostic systems are often complex and exhibit limited performance on liver diseases, particularly the liver cancer. This paper presents a fuzzy decision support system for helping students diagnose some human liver diseases in educational medical institutions. The proposed system aims to improve real medical diagnosis processes. The approach has three basic steps: 1) symptoms-based diagnosis, 2) liver function-based diagnosis, and 3) image processingbased diagnosis. The proposed system employs two artificial intelligence techniques: fuzzy logic and image processing. The first is used for diagnosing liver diseases based on the liver function tests, while the second is used for diagnosing liver diseases such as the liver cancer, hepatitis, liver cirrhosis, liver fibrosis, and fatty liver. The proposed system combines two methods: the Mamdani inference and simulation method used in the MATLAB17 fuzzy logic toolbox, and the gray level co-occurrence matrix, for extracting the features of the secondorder statistical texture of images acquired using computed tomography, magnetic resonance imaging, or ultrasound, for various liver diseases. Our results reveal a very good agreement between expert-made and system-made diagnoses, suggesting high accuracy.

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The ability to adapt and utilize emergency facilities is a critical element in responding to surges resulting from man-made and natural events resulting in mass casualties. Emergency services must be adequately prepared to effectively absorb a sudden increase in patients and to allow the medical staff to operate effectively Objectives : To qualitatively describe patient, hospital care, and clinical pathway characteristics that may be associated with pathway effectiveness in an emergency department. Methods :The current study is retrospective, cross-sectional and descriptive study the flow of clinical pathways in an emergency hospital is described in case of an acute mass trauma accident involving victims. Drawbacks and pathway laggings and redundancies were presented and discussed. A list of characteristics that might impact clinical pathway effectiveness was developed .A hypothesis-driven qualitative analysis was used to describe key characteristics that might differentiate effective from ineffective critical pathways. Results: The care pathway adopted during the discrete event under study was perceived to be redundant by the management and by the working staff. Laggings of sequential steps and interventions were inevitable. The patient total times in the department was affected and had effects on medical services performance. Conclusions: Recommendations for improving the effectiveness of the emergency services of the hospital under study were given. The adoption of clinical pathways may be a very promising method to improve the emergency department service at a hospital and so to react to the challenges of massive surges in patient care. Principles of care maps when integrated into the early stage of planning and design of a building and a service are expected to facilitate modifications to be implemented at modest capital cost increases for the benefit of both high-risk patients and over-stressed Health Care Workers.