title: Bayesian Machine Learning Techniques for revealing complex interactions among genetic and clinical factors in association with extra-intestinal Manifestations in IBD patients.
abstract: The objective of the study is to assess the predictive performance of three different techniques as classifiers for extra-intestinal manifestations in 152 patients with Crohn's disease. Naïve Bayes, Bayesian Additive Regression Trees and Bayesian Networks implemented using a Greedy Thick Thinning algorithm for learning dependencies among variables and EM algorithm for learning conditional probabilities associated to each variable are taken into account. Three sets of variables were considered: (i) disease characteristics: presentation, behavior and location (ii) risk factors: age, gender, smoke and familiarity and (iii) genetic polymorphisms of the NOD2, CD14, TNFA, IL12B, and IL1RN genes, whose involvement in Crohn's disease is known or suspected. Extra-intestinal manifestations occurred in 75 patients. Bayesian Networks achieved accuracy of 82% when considering only clinical factors and 89% when considering also genetic information, outperforming the other techniques. CD14 has a small predicting capability. Adding TNFA, IL12B to the 3020insC NOD2 variant improved the accuracy.
authors.affiliation: Unit of Biostatistics, Epidemiology and Public Health, University of Padova, Italy.
authors.forename: E
authors.initials: E
authors.lastname: Menti
authors.affiliation: Unit of Biostatistics, Epidemiology and Public Health, University of Padova, Italy.
authors.forename: C
authors.initials: C
authors.lastname: Lanera
authors.affiliation: Unit of Biostatistics, Epidemiology and Public Health, University of Padova, Italy.
authors.forename: G
authors.initials: G
authors.lastname: Lorenzoni
authors.affiliation: Medical Genetics Unit, Department of Clinical and Biological Sciences, University of Torino, Italy.
authors.forename: Daniela F
authors.initials: DF
authors.lastname: Giachino
authors.affiliation: Medical Genetics Unit, Department of Clinical and Biological Sciences, University of Torino, Italy.
authors.forename: Mario De
authors.initials: M
authors.lastname: Marchi
authors.affiliation: Unit of Biostatistics, Epidemiology and Public Health, University of Padova, Italy.
authors.forename: Dario
authors.initials: D
authors.lastname: Gregori
authors.affiliation: Medical Statistics Unit, Department of Clinical and Biological Sciences, University of Torino, Italy.
authors.forename: Paola
authors.initials: P
authors.lastname: Berchialla
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003424:Crohn Disease / Q000150:complications* / Q000235:genetics*; D057225:Data Mining; D005260:Female; D020022:Genetic Predisposition to Disease; D006801:Humans; D000069550:Machine Learning; D008297:Male; D015233:Models, Statistical; D011110:Polymorphism, Genetic; D012307:Risk Factors
keywords: Clinical Decision Support; Clinical research informatics; Data mining and statistical data analysis