A Dynamic Bayesian Network model for long-term simulation of clinical complications in type 1 diabetes.

The increasing prevalence of diabetes and its related complications is raising the need for effective methods to predict patient evolution and for stratifying cohorts in terms of risk of developing diabetes-related complications. In this paper, we present a novel approach to the simulation of a type 1 diabetes population, based on Dynamic Bayesian Networks, which combines literature knowledge with data mining of a rich longitudinal cohort of type 1 diabetes patients, the DCCT/EDIC study. In particular, in our approach we simulate the patient health state and complications through discretized variables.

Bag of Naïve Bayes: biomarker selection and classification from genome-wide SNP data.

Background: Multifactorial diseases arise from complex patterns of interaction between a set of genetic traits and the environment. To fully capture the genetic biomarkers that jointly explain the heritability component of a disease, thus, all SNPs from a genome-wide association study should be analyzed simultaneously.

Results: In this paper, we present Bag of Naïve Bayes (BoNB), an algorithm for genetic biomarker selection and subjects classification from the simultaneous analysis of genome-wide SNP data.

A new index to optimally design and compare continuous glucose monitoring glucose prediction algorithms.

Background: Continuous glucose monitoring (CGM) data can be exploited to prevent hypo-/hyperglycemic events in real time by forecasting future glucose levels. In the last few years, several glucose prediction algorithms have been proposed, but how to compare them (e.g.