Abstract
Mathematical models are prevalent in modern medicine. However, reasoning with realistic biomedical models is computationally demanding as parameters are typically subject to nonlinear relations, dynamic behavior, and uncertainty. This paper addresses this problem by proposing a new framework based on constraint programming for a sound propagation of uncertainty from model parameters to results. We apply our approach to an important problem in the obesity research field, the estimation of free-living energy intake in humans. Complementary to alternative solutions, our approach is able to correctly characterize the provided estimates given the uncertainty inherent to the model parameters.
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Franco, A., Correia, M., Cruz, J. (2015). Uncertainty Propagation in Biomedical Models. In: Holmes, J., Bellazzi, R., Sacchi, L., Peek, N. (eds) Artificial Intelligence in Medicine. AIME 2015. Lecture Notes in Computer Science(), vol 9105. Springer, Cham. https://doi.org/10.1007/978-3-319-19551-3_21
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DOI: https://doi.org/10.1007/978-3-319-19551-3_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19550-6
Online ISBN: 978-3-319-19551-3
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