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.
Original language | Unknown |
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Title of host publication | Lecture Notes in Artificial Intelligence |
Pages | 324-334 |
Volume | 2780 |
DOIs | |
Publication status | Published - 1 Jan 2003 |
Event | 9th Conference on Artificial Intelligence in Medicine in Europe: AIME 2003 - Protaras, Cyprus Duration: 18 Oct 2003 → 22 Oct 2003 |
Conference
Conference | 9th Conference on Artificial Intelligence in Medicine in Europe |
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Country/Territory | Cyprus |
City | Protaras |
Period | 18/10/03 → 22/10/03 |