Uncertainty propagation in biomedical models

Andrea Franco; Marco Correia; Jorge Cruz

doi:10.1007/978-3-319-19551-3_21

Uncertainty propagation in biomedical models

Andrea Franco, Marco Correia, Jorge Cruz

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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 language	English
Title of host publication	Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings
Publisher	Springer Verlag
Pages	166-171
Number of pages	6
Volume	9105
ISBN (Electronic)	978-3-319-19551-3
DOIs	https://doi.org/10.1007/978-3-319-19551-3_21
Publication status	Published - 2015
Event	15th Conference on Artificial Intelligence in Medicine, AIME 2015 - Pavia, Italy Duration: 17 Jun 2015 → 20 Jun 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9105
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	15th Conference on Artificial Intelligence in Medicine, AIME 2015
Country/Territory	Italy
City	Pavia
Period	17/06/15 → 20/06/15

Keywords

ENERGY-INTAKE
Artificial Intelligence
Medical Informatics
Robotics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-19551-3_21

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

Franco, Andrea ; Correia, Marco ; Cruz, Jorge. / Uncertainty propagation in biomedical models. Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings. Vol. 9105 Springer Verlag, 2015. pp. 166-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Uncertainty propagation in biomedical models",

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.",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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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, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9105, Springer Verlag, pp. 166-171, 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, 17/06/15. https://doi.org/10.1007/978-3-319-19551-3_21

Uncertainty propagation in biomedical models. / Franco, Andrea; Correia, Marco; Cruz, Jorge.
Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings. Vol. 9105 Springer Verlag, 2015. p. 166-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9105).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N1 - sem pdf conforme despacho.

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N2 - 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.

AB - 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.

KW - ENERGY-INTAKE

KW - Artificial Intelligence

KW - Medical Informatics

KW - Robotics

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DO - 10.1007/978-3-319-19551-3_21

M3 - Conference contribution

AN - SCOPUS:84947937714

VL - 9105

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Artificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings

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T2 - 15th Conference on Artificial Intelligence in Medicine, AIME 2015

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ER -

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

Uncertainty propagation in biomedical models

Abstract

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Conference

Keywords

UN SDGs

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