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Abstract:
In many medical settings precise numerical data for all findings of interest either is unavailable or is too timeconsuming to measure. In such situations where a mixture of exact and approximate data is available, a model which can reason with such data and produce output of precision proportional to the the precision of the available findings is desirable. We are developing an acute cardiovascular model which can reason with mixed qualitative and quantitative data, the principles of which can be applied to any mixed-data situation. Our objective is to construct a computationally efficient, first-principles model of the cardiovascular system's response to blood loss and fluid replacement. Such a model can be used to estimate the volume of blood loss within therapeutically acceptable ranges and predict the
Citations
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