Computers in biology and medicine
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This work proposes the application of neural network multi-models to the prediction of adverse acute hypotensive episodes (AHE) occurring in intensive care units (ICU). A generic methodology consisting of two phases is considered. In the first phase, a correlation analysis between the current blood pressure time signal and a collection of historical blood pressure templates is carried out. ⋯ A correct prediction of 10 out of 10 AHE for event 1 and of 37 out of 40 AHE for event 2 was achieved, corresponding to the best results of all entries in the two events of the challenge. The generalization capabilities of the strategy was confirmed by applying it to an extended dataset of blood pressure signals, also collected from the MIMIC-II database. A total of 2344 examples, selected from 311 blood pressure signals were tested, enabling to obtain a global sensitivity of 82.8% and a global specificity of 78.4%.
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Doctors applying mechanical ventilation need to find the best balance between benefit and risk for the patient. Mathematical models simulating patient's reactions to alterations in the ventilation regime may be employed. ⋯ The interaction of model systems reveals qualitatively varying results depending on the complexity of the involved models. Realistic overlaying of respiratory and cardiovascular rhythms can be detected in blood gas concentrations.