Objectives: To determine whether nurses, using the WHO/UNICEF algorithm for integrated management of childhood illness (IMCI), modified to include dengue infection, satisfactorily classified children in an area endemic for dengue haemorrhagic fever (DHF).
Methods: Nurses assessed and classified, using the modified IMCI algorithm, a systematic sample of 1250 children aged 2 months to 10 years (n = 1250) presenting to a paediatric hospital in Dong Nai Province, Vietnam. Their classification was compared with that of a paediatrician, blind to the result of the nurses' assessment, which could be modified in the light of simple investigations, e.g. dengue serology.
Results: In children aged 2-59 months (n = 859), the nurses were able to classify, using the modified chart, the presenting illness in >99% of children and found more than one classification in 70%. For the children with pneumonia, diarrhoea, dengue shock syndrome, severe DHF and severe disease requiring urgent admission, the nurse's classification was >60% sensitive and >85% specific compared with that of the paediatrician. For the nurse's classification of DHF the specificity was 50-55% for the children <5 years and in children with definitive dengue serology. Alterations in the DHF algorithm improved specificity at the expense of sensitivity.
Conclusion: Using the IMCI chart, nurses classified appropriately many of the major clinical problems in sick children <5 years in southern Vietnam. However, further modifications will be required in the fever section, particularly for dengue. The impact of using the IMCI chart in peripheral health stations remains to be evaluated.
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http://dx.doi.org/10.1111/j.1365-3156.2004.01232.x | DOI Listing |
JMIR Form Res
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Department of Computer Science, University of California, Irvine, Irvine, CA, United States.
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Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology Slovak University of Technology in Bratislava, Bratislava, Slovakia.
This paper introduces a novel approach for the offline estimation of stationary moving average processes, further extending it to efficient online estimation of non-stationary processes. The novelty lies in a unique technique to solve the autocorrelation function matching problem leveraging that the autocorrelation function of a colored noise is equal to the autocorrelation function of the coefficients of the moving average process. This enables the derivation of a system of nonlinear equations to be solved for estimating the model parameters.
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School of Information and Communication Engineering, Beijing University of Information Science and Technology, Bei Jing City, China.
To enhance the intelligent classification of computer vulnerabilities and improve the efficiency and accuracy of network security management, this study delves into the application of a comprehensive classification system that integrates the Memristor Neural Network (MNN) and an improved Temporal Convolutional Neural Network (TCNN) in network security management. This system not only focuses on the precise classification of vulnerability data but also emphasizes its core role in strengthening the network security management framework. Firstly, the study designs and implements a neural network model based on memristors.
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Department of Electrical Engineering, College of Engineering, Taif University, Taif, Saudi Arabia.
Modernizing power systems into smart grids has introduced numerous benefits, including enhanced efficiency, reliability, and integration of renewable energy sources. However, this advancement has also increased vulnerability to cyber threats, particularly False Data Injection Attacks (FDIAs). Traditional Intrusion Detection Systems (IDS) often fall short in identifying sophisticated FDIAs due to their reliance on predefined rules and signatures.
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