Constraint reasoning in deep biomedical models.
OBJECTIVE
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. The objective of this work is to propose a constraint reasoning framework to support safe decisions based on deep biomedical models.


METHODS
The methods used in our approach include the generic constraint propagation techniques for reducing the bounds of uncertainty of the numerical variables complemented with new constraint reasoning techniques that we developed to handle differential equations.


RESULTS
The results of our approach are illustrated in biomedical models for the diagnosis of diabetes, tuning of drug design and epidemiology where it was a valuable decision-supporting tool notwithstanding the uncertainty on data.


CONCLUSIONS
The main conclusion that follows from the results is that, in biomedical decision support, constraint reasoning may be a worthwhile alternative to traditional simulation methods, especially when safe decisions are required.
Cruz
Jorge
J
Departamento de Informática, Centro de Inteligência Artificial, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal. jc@di.fct.unl.pt
Barahona
Pedro
P
D001185:Artificial Intelligence; D003920:Diabetes Mellitus / Q000175:diagnosis; D004196:Disease Outbreaks; D015195:Drug Design; D006801:Humans; D008954:Models, Biological; D010599:Pharmacokinetics