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• W2087452364 abstract "We present a classifier for use as a decision assist tool to identify a hypovolemic state in trauma patients during helicopter transport to a hospital, when reliable acquisition of vital-sign data may be difficult. The decision tool uses basic vital-sign variables as input into linear classifiers, which are then combined into an ensemble classifier. The classifier identifies hypovolemic patients with an area under a receiver operating characteristic curve (AUC) of 0.76 (standard deviation 0.05, for 100 randomly-reselected patient subsets). The ensemble classifier is robust; classification performance degrades only slowly as variables are dropped, and the ensemble structure does not require identification of a set of variables for use as best-feature inputs into the classifier. The ensemble classifier consistently outperforms best-features-based linear classifiers (the classification AUC is greater, and the standard deviation is smaller, p < 0.05). The simple computational requirements of ensemble classifiers will permit them to function in small fieldable devices for continuous monitoring of trauma patients." @default.
• W2087452364 created "2016-06-24" @default.
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• W2087452364 date "2008-06-01" @default.
• W2087452364 modified "2023-09-30" @default.
• W2087452364 title "Decision tool for the early diagnosis of trauma patient hypovolemia" @default.
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• W2087452364 doi "https://doi.org/10.1016/j.jbi.2007.12.002" @default.
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