Uncertainty propagation in biomedical models

Andrea Franco, Marco Correia, Jorge Cruz

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

Abstract

Mathematical models are prevalent in modern medicine. However, reasoning with realistic biomedical models is computationally demanding as parameters are typically subject to nonlinear relations, dynamic behavior, and uncertainty. This paper addresses this problem by proposing a new framework based on constraint programming for a sound propagation of uncertainty from model parameters to results. We apply our approach to an important problem in the obesity research field, the estimation of free-living energy intake in humans. Complementary to alternative solutions, our approach is able to correctly characterize the provided estimates given the uncertainty inherent to the model parameters.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings
PublisherSpringer-Verlag
Pages166-171
Number of pages6
Volume9105
ISBN (Electronic)978-3-319-19551-3
DOIs
Publication statusPublished - 2015
Event15th Conference on Artificial Intelligence in Medicine, AIME 2015 - Pavia, Italy
Duration: 17 Jun 201520 Jun 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9105
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference15th Conference on Artificial Intelligence in Medicine, AIME 2015
CountryItaly
CityPavia
Period17/06/1520/06/15

Keywords

  • ENERGY-INTAKE
  • Artificial Intelligence
  • Medical Informatics
  • Robotics

Fingerprint Dive into the research topics of 'Uncertainty propagation in biomedical models'. Together they form a unique fingerprint.

  • Cite this

    Franco, A., Correia, M., & Cruz, J. (2015). Uncertainty propagation in biomedical models. In Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings (Vol. 9105, pp. 166-171). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9105). Springer-Verlag. https://doi.org/10.1007/978-3-319-19551-3_21