Deep biomedical models are often expressed by means of differential equations. Despite their expressive power, they are difficult to reason about and make decisions, given their non-linearity and the important effects that the uncertainty on data may cause. For this reason traditional numerical simulations may only provide a likelihood of the results obtained. In contrast, we propose in this paper the use of a constraint reasoning framework able to make safe decision notwithstanding some degree of uncertainty, and illustrate this approach in the diagnosis of diabetes and the tuning of drug design. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Cruz, J., & Barahona, P. (2003). Constraint reasoning in deep biomedical models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2780 LNAI, pp. 324–334). https://doi.org/10.1007/978-3-540-39907-0_44
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