Reasoning with uncertainty in biomedical models

Andrea Franco, Marco Correia, Jorge Cruz

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

Abstract

The use of mathematical models in biomedical research largely developed in the second half of the 20th century. However, their translation to clinically useful tools has proved challenging. Reasoning with deep biomedical models is computationally demanding as parameters are typically subject to nonlinear relations, dynamic behavior, and uncertainty. This paper proposes a new approach for assessing the reliability of the conclusions drawn from these models given the underlying uncertainty. It relies on probabilistic constraint programming for a sound propagation of uncertainty from model parameters to results. The advantages of the approach are illustrated on an important problem in the obesity research field, namely the estimation of free-living energy intake in humans. Based on a well known energy intake model, our approach is able to correctly characterize the provided estimates given the uncertainty inherent to the model parameters.

Original languageEnglish
Title of host publicationProgress in Artificial Intelligence - 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Proceedings
PublisherSpringer-Verlag
Pages41-53
Number of pages13
Volume9273
ISBN (Electronic)978-3-319-23485-4
DOIs
Publication statusPublished - 2015
Event17th Portuguese Conference on Artificial Intelligence, EPIA 2015 - Coimbra, Portugal
Duration: 8 Sep 201511 Sep 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9273
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference17th Portuguese Conference on Artificial Intelligence, EPIA 2015
CountryPortugal
CityCoimbra
Period8/09/1511/09/15

Keywords

  • Biomedical models
  • Constraint programming
  • Energy intake

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