J Med Syst
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A regional telemedicine hub, providing linkage of a telemedicine command center with an extended network of clinical experts in the setting of a natural or intentional disaster, may facilitate future disaster response and improve patient outcomes. However, the health benefits derived from the use of telemedicine in disaster response have not been quantitatively analyzed. In this paper, we present a general model of the application of telemedicine to disaster response and evaluate a concept of operations for a regional telemedicine hub, which would create distributed surge capacity using regional telemedicine networks connecting available healthcare and telemedicine infrastructures to external expertise. Specifically, we investigate (1) the scope of potential use of telemedicine in disaster response; (2) the operational characteristics of a regional telemedicine hub using a new discrete-event simulation model of an earthquake scenario; and (3) the benefit that the affected population may gain from a coordinated regional telemedicine network.
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In this paper, we present a three-stage expert system based on a hybrid support vector machines (SVM) approach to diagnose thyroid disease. Focusing on feature selection, the first stage aims at constructing diverse feature subsets with different discriminative capability. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the designed SVM classifier for training an optimal predictor model whose parameters are optimized by particle swarm optimization (PSO). ⋯ Experimental results demonstrate that FS-PSO-SVM significantly outperforms the other ones. In addition, Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far by 10-fold cross-validation (CV) method, with the mean accuracy of 97.49% and with the maximum accuracy of 98.59%. Promisingly, the proposed FS-PSO-SVM expert system might serve as a new candidate of powerful tools for diagnosing thyroid disease with excellent performance.
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Accurate Diagnosis of lung disease depends on understanding the sounds emanating from lung and its location. Lung sounds are of significance as they supply precise and important information on the health of the respiratory system. In addition, correct interpretation of breath sounds depends on a systematic approach to auscultation; it also requires the ability to describe the location of abnormal finding in relation to bony structures and anatomic landmark lines. ⋯ Adventitious sounds from different location can be detected. It is common to seek confirmation of the sound detection and its location using invasive and potentially harmful imaging diagnosis techniques like x-rays. To overcome this limitation and for fast, reliable, accurate, and inexpensive diagnose a technique is developed in this research for identifying the location of infection through a computerized auscultation system.
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Under the constraints of limited medical resources and severe competition among hospitals, administrators have begun to pay attention to the opportunities of cost reduction and quality improvement in hospital management, in order to find methods to increase hospital revenue and improve service quality. The operating room should be one of the most important sources of hospital income, yet it is both costly to run and constrictive to inpatient flow. Successful hospital management necessitates the construction of cost-effective and quality operating room scheduling. ⋯ Urgent revisions to the model in consideration of such factors as doctor's availability, outpatient consulting hours and unfavorable surgery hours can be achieved in a timely manner. With the present approach, surgical procedures can start punctually, inpatient waiting time for surgery and length of stay can be reduced, and staff morale can be enhanced. These improvements will result in cost reduction, and increased hospital revenue without sacrificing the quality of medical care.
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The paper attempts to improve the accuracy of a fuzzy expert decision making system by tuning the parameters of type-2 sigmoid membership functions of fuzzy input variables and hence determining the most appropriate type-1 membership function. The current work mathematically models the variability of human decision making process using type-2 fuzzy sets. Moreover, an index of accuracy of a fuzzy expert system has been proposed and determined analytically. ⋯ Based on the accuracy estimations applied over a set of pathophysiological parameters, viz. body mass index, glucose, urea, creatinine, systolic and diastolic blood pressure, appropriate type-1 fuzzy sets of these parameters have been determined assuming normal distribution of type-1 membership function values in type-2 fuzzy sets. The type-1 fuzzy sets so determined have been used to develop an FPGA based smart processor. Using the processor, renal diagnosis of patients has been performed with an accuracy of 98.75%.