TY - GEN
T1 - Reasoning with uncertainty in biomedical models
AU - Franco, Andrea
AU - Correia, Marco
AU - Cruz, Jorge
N1 - sem pdf conforme despacho
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Biomedical models
KW - Constraint programming
KW - Energy intake
UR - http://www.scopus.com/inward/record.url?scp=84945980166&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-23485-4_5
DO - 10.1007/978-3-319-23485-4_5
M3 - Conference contribution
AN - SCOPUS:84945980166
VL - 9273
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 41
EP - 53
BT - Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Proceedings
PB - Springer Verlag
T2 - 17th Portuguese Conference on Artificial Intelligence, EPIA 2015
Y2 - 8 September 2015 through 11 September 2015
ER -