A Bayesian Network for Probabilistic Reasoning and Imputation of Missing Risk Factors in Type 2 Diabetes View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Francesco Sambo , Andrea Facchinetti , Liisa Hakaste , Jasmina Kravic , Barbara Di Camillo , Giuseppe Fico , Jaakko Tuomilehto , Leif Groop , Rafael Gabriel , Tuomi Tiinamaija , Claudio Cobelli

ABSTRACT

We propose a novel Bayesian network tool to model the probabilistic relations between a set of type 2 diabetes risk factors. The tool can be used for probabilistic reasoning and for imputation of missing values among risk factors. The Bayesian network is learnt from a joint training set of three European population studies. Tested on an independent patient set, the network is shown to be competitive with both a standard imputation tool and a widely used risk score for type 2 diabetes, providing in addition a richer description of the interdependencies between diabetes risk factors. More... »

PAGES

172-176

Book

TITLE

Artificial Intelligence in Medicine

ISBN

978-3-319-19550-6
978-3-319-19551-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-19551-3_22

DOI

http://dx.doi.org/10.1007/978-3-319-19551-3_22

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1031144829


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