An ignorant belief network to forecast glucose concentration from clinical databases. Ramoni, M.; Riva, A.; Stefanelli, M.; and Patel, V. Artificial Intelligence in Medicine, 7(6):541-559, 12, 1995.
An ignorant belief network to forecast glucose concentration from clinical databases [link]Website  abstract   bibtex   
Ignorant Belief Networks (IBNs) are a class of Bayesian Belief Networks (BBNs) able to reason on the basis of incomplete probabilistic information and to incrementally refine the precision of the inferred probabilities as more information becomes available. In this paper, we will describe how can be used to develop a system able to forecast blood glucose concentration in patients affected by insulin dependent diabetes mellitus (IDDM). The major difference between our approach and the traditional ones is that probability distributions over the IBN are not provided by some human expert or by the current literature but they are directly extracted from a clinical database of IDDM patients. This choice capitalizes on the large amount of information generated by the daily control of blood glucose and allows the system to improve the accuracy of predictions as more information becomes available. We will show how, even with a very small subset of the information needed to specify a BBN, the IBN is able to carry out predictions about the future blood glucose concentration in a patient by explicitly taking into consideration the level of ignorance embedded in the network.
@article{
 title = {An ignorant belief network to forecast glucose concentration from clinical databases},
 type = {article},
 year = {1995},
 identifiers = {[object Object]},
 keywords = {Bayesian belief networks,Insulin dependent diabetes mellitus,Machine learning},
 pages = {541-559},
 volume = {7},
 websites = {http://www.sciencedirect.com/science/article/pii/0933365795000261},
 month = {12},
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 abstract = {Ignorant Belief Networks (IBNs) are a class of Bayesian Belief Networks (BBNs) able to reason on the basis of incomplete probabilistic information and to incrementally refine the precision of the inferred probabilities as more information becomes available. In this paper, we will describe how can be used to develop a system able to forecast blood glucose concentration in patients affected by insulin dependent diabetes mellitus (IDDM). The major difference between our approach and the traditional ones is that probability distributions over the IBN are not provided by some human expert or by the current literature but they are directly extracted from a clinical database of IDDM patients. This choice capitalizes on the large amount of information generated by the daily control of blood glucose and allows the system to improve the accuracy of predictions as more information becomes available. We will show how, even with a very small subset of the information needed to specify a BBN, the IBN is able to carry out predictions about the future blood glucose concentration in a patient by explicitly taking into consideration the level of ignorance embedded in the network.},
 bibtype = {article},
 author = {Ramoni, Marco and Riva, Alberto and Stefanelli, Mario and Patel, Vimla},
 journal = {Artificial Intelligence in Medicine},
 number = {6}
}
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