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.
|Title of host publication||Lecture Notes in Artificial Intelligence|
|Publication status||Published - 1 Jan 2003|
|Event||9th Conference on Artificial Intelligence in Medicine in Europe - |
Duration: 1 Jan 2003 → …
|Conference||9th Conference on Artificial Intelligence in Medicine in Europe|
|Period||1/01/03 → …|