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Constraint Reasoning in Deep Biomedical Models

  • Jorge Cruz
  • Pedro Barahona
Conference paper
  • 443 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2780)

Abstract

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.

Keywords

Constraint Satisfaction Problem Variable Domain Expressive Power Initial Domain Safe Decision 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jorge Cruz
    • 1
  • Pedro Barahona
    • 1
  1. 1.Centro de Inteligência Artificial, Departamento de InformáticaUniversidade Nova de LisboaCaparicaPortugal

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