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. Two types of models are presented, one entirely learned from the data and the other partially driven by literature derived knowledge. The whole cohort is simulated for fifteen years, and the simulation error (i.e. for each variable, the percentage of patients predicted in the wrong state) is calculated every year on independent test data. For each variable, the population predicted in the wrong state is below 10% on both models over time. Furthermore, the distributions of real vs. simulated patients greatly overlap. Thus, the proposed models are viable tools to support decision making in type 1 diabetes.
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy.
Simone
S
Marini
Department of Information Engineering, University of Padova, Italy.
Emanuele
E
Trifoglio
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy.
Nicola
N
Barbarini
Department of Information Engineering, University of Padova, Italy.
Francesco
F
Sambo
Department of Information Engineering, University of Padova, Italy.
Barbara
B
Di Camillo
IRCCS Fondazione S. Maugeri, Pavia, Italy.
Alberto
A
Malovini
Department of Information Engineering, University of Padova, Italy.
Marco
M
Manfrini
Department of Information Engineering, University of Padova, Italy.
Claudio
C
Cobelli
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy.
Riccardo
R
Bellazzi
D001499:Bayes Theorem; D003198:Computer Simulation; D057225:Data Mining; D048909:Diabetes Complications; D003922:Diabetes Mellitus, Type 1; D006801:Humans
CVD; Dynamic Bayesian Network; Nephropaty; Simulation; Tabu search; Type 1 diabetes