Modelling an outpatient unit in a clinical health centre using discrete event simulation

This paper describes a project paper of a simulation modelling course.It presents the potential of computer simulation in modelling the current performance of an outpatient department of a clinical health centre in a rural area.The model was run using Arena student version 14.5.From the 60 replication length run, the obtained result shows that the patient’s waiting time is 26.4 minutes, which is lesser than the established standard waiting time of 30 minutes

Performance of image enhancement methods for diabetic retinopathy based on retinal fundus image

In Malaysia, Non-mydriatic fundus camera become a primary tool for Diabetic Retinopathy screening protocols due to user friendly and cost effective procedure. However, the quality of fundus image produces often suffer from uneven illumination, color distortion, blur, and low contrast. Therefore, the need for image enhancement become crucial to be implemented as a pre-processing technique in image processing funnel. This paper presents six general basic methods that commonly applied for image enhancement which includes histogram equalization, contrast stretching, image negative, brightness enhances, low light image and gray level slicing. The performance evaluation of each method compared based on human interpretations and quantitative measurement using MSE, PSNR and entropy. Retinal fundus images collected from Ophthalmology Clinic, Hospital Universiti Sains Malaysia were used as the input images. Quantitative and qualitative result shows that CS method become the preferred method to be used for image enhancement of retinal fundus image in Diabetic Retinopathy

A fuzzy rule-based expert system for asthma severity identification in emergency department

The emergency department (ED) of a hospital is an important unit that deals with time-sensitive and life-threatening medical cases. Rapid treatment and accuracy in diagnosis are considered the main characteristics of excellent operational processes in ED. However, in reality, long waiting time and uncertainty in the diagnosis has affected the quality of ED services. Nonetheless, these problems can be improved by utilising computing technologies that assist medical professionals to make fast and accurate decisions. This paper investigates the issues of under-treatment and uncertainty condition of acute asthma cases in ED. A novel approach, known as the fuzzy logic principle is employed to determine the severity of acute asthma. The fuzzy set theory, known as Fuzzy Rule-based Expert System for Asthma Severity (FRESAS) determination is embedded into the expert system (ES) to assess the severity of asthma among patients in ED. The proposed fuzzy methodology effectively manages the fuzziness of the patient’s information data, and determines the subjective judgment of medical practitioners’ level on eight criteria assessed in severity determination. Knowledge acquisition and representation, fuzzification, fuzzy inference engine, and defuzzification are the processes tested by the FRESAS development that incorporates expert advice. The system evaluation is performed by using datasets that were extracted from the ED clerking notes from one of the hospitals in Northern Peninsular Malaysia.System evaluation demonstrates that the proposed system performs efficiently in determining the severity of acute asthma. Furthermore, the proposed system offers opportunities for further research on other types of diseases in ED, and improves other hybridisation approaches