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• W2913261640 abstract "Real-time health surveillance becomes important and necessary with the increase of the elderly population to preserve their quality of life. Real-time models aim to provide alerts before the severe illness occurs. Acute respiratory distress syndrome is a crucial disease of the respiratory system that threats the health of the elderly. This paper proposes a real-time model for the surveillance of ARDS based on belief functions theory. Non-invasive physiological signals are considered such as heart rate, respiratory rate, oxygen saturation and mean airway blood pressure. Different linear and nonlinear parameters are extracted from these signals; then a parameters selection procedure is performed to reduce their dimensionality. Afterwards, classifiers are constructed using parameters distributions defined in the evidence framework. Real-time prediction is then performed by combining all classifiers decisions. As results, high performances are obtained over the testing sets with performances of 77% and 71% for sensitivity and specificity, respectively." @default.
• W2913261640 created "2019-02-21" @default.
• W2913261640 creator A5030246194 @default.
• W2913261640 creator A5068661200 @default.
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• W2913261640 date "2019-04-01" @default.
• W2913261640 modified "2023-10-11" @default.
• W2913261640 title "Evidence-based model for real-time surveillance of ARDS" @default.
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• W2913261640 doi "https://doi.org/10.1016/j.bspc.2019.01.016" @default.
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