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The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn’s Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions?

1
Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy
2
Department of Clinical and Biological Sciences, University of Torino, 10126 Torino, Italy
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2019, 8(6), 865; https://doi.org/10.3390/jcm8060865
Received: 3 May 2019 / Revised: 12 June 2019 / Accepted: 13 June 2019 / Published: 17 June 2019
(This article belongs to the Section Gastroenterology & Hepatopancreatobiliary Medicine)
(1) Background: The high heterogeneity of inflammatory bowel disease (IBD) makes the study of this condition challenging. In subjects affected by Crohn’s disease (CD), extra-intestinal manifestations (EIMs) have a remarkable potential impact on health status. Increasing numbers of patient characteristics and the small size of analyzed samples make EIMs prediction very difficult. Under such constraints, Bayesian machine learning techniques (BMLTs) have been proposed as a robust alternative to classical models for outcome prediction. This study aims to determine whether BMLT could improve EIM prediction and statistical support for the decision-making process of clinicians. (2) Methods: Three of the most popular BMLTs were employed in this study: Naϊve Bayes (NB), Bayesian Network (BN) and Bayesian Additive Regression Trees (BART). They were applied to a retrospective observational Italian study of IBD genetics. (3) Results: The performance of the model is strongly affected by the features of the dataset, and BMLTs poorly classify EIM appearance. (4) Conclusions: This study shows that BMLTs perform worse than expected in classifying the presence of EIMs compared to classical statistical tools in a context where mixed genetic and clinical data are available but relevant data are also missing, as often occurs in clinical practice. View Full-Text
Keywords: Crohn’s disease; extra-intestinal manifestation; risk prediction; Bayesian methods; machine learning techniques Crohn’s disease; extra-intestinal manifestation; risk prediction; Bayesian methods; machine learning techniques
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MDPI and ACS Style

Bottigliengo, D.; Berchialla, P.; Lanera, C.; Azzolina, D.; Lorenzoni, G.; Martinato, M.; Giachino, D.; Baldi, I.; Gregori, D. The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn’s Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions? J. Clin. Med. 2019, 8, 865. https://doi.org/10.3390/jcm8060865

AMA Style

Bottigliengo D, Berchialla P, Lanera C, Azzolina D, Lorenzoni G, Martinato M, Giachino D, Baldi I, Gregori D. The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn’s Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions? Journal of Clinical Medicine. 2019; 8(6):865. https://doi.org/10.3390/jcm8060865

Chicago/Turabian Style

Bottigliengo, Daniele, Paola Berchialla, Corrado Lanera, Danila Azzolina, Giulia Lorenzoni, Matteo Martinato, Daniela Giachino, Ileana Baldi, and Dario Gregori. 2019. "The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn’s Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions?" Journal of Clinical Medicine 8, no. 6: 865. https://doi.org/10.3390/jcm8060865

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