Constraint reasoning in deep biomedical models

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageUnknown
Title of host publicationLecture Notes in Artificial Intelligence
Pages324-334
Volume2780
DOIs
Publication statusPublished - 1 Jan 2003
Event9th Conference on Artificial Intelligence in Medicine in Europe: AIME 2003 - Protaras, Cyprus
Duration: 18 Oct 200322 Oct 2003

Conference

Conference9th Conference on Artificial Intelligence in Medicine in Europe
CountryCyprus
CityProtaras
Period18/10/0322/10/03

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