The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn’s Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions?
(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...
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doaj-2d07cd573d7a4e76b04ef4d72ee93ab72020-11-24T21:15:54ZengMDPI AGJournal of Clinical Medicine2077-03832019-06-018686510.3390/jcm8060865jcm8060865The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn’s Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions?Daniele Bottigliengo0Paola Berchialla1Corrado Lanera2Danila Azzolina3Giulia Lorenzoni4Matteo Martinato5Daniela Giachino6Ileana Baldi7Dario Gregori8Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, 35131 Padova, ItalyDepartment of Clinical and Biological Sciences, University of Torino, 10126 Torino, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, 35131 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, 35131 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, 35131 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, 35131 Padova, ItalyDepartment of Clinical and Biological Sciences, University of Torino, 10126 Torino, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, 35131 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy(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.https://www.mdpi.com/2077-0383/8/6/865Crohn’s diseaseextra-intestinal manifestationrisk predictionBayesian methodsmachine learning techniques |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Daniele Bottigliengo Paola Berchialla Corrado Lanera Danila Azzolina Giulia Lorenzoni Matteo Martinato Daniela Giachino Ileana Baldi Dario Gregori |
spellingShingle |
Daniele Bottigliengo Paola Berchialla Corrado Lanera Danila Azzolina Giulia Lorenzoni Matteo Martinato Daniela Giachino Ileana Baldi Dario Gregori 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 Crohn’s disease extra-intestinal manifestation risk prediction Bayesian methods machine learning techniques |
author_facet |
Daniele Bottigliengo Paola Berchialla Corrado Lanera Danila Azzolina Giulia Lorenzoni Matteo Martinato Daniela Giachino Ileana Baldi Dario Gregori |
author_sort |
Daniele Bottigliengo |
title |
The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn’s Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions? |
title_short |
The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn’s Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions? |
title_full |
The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn’s Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions? |
title_fullStr |
The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn’s Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions? |
title_full_unstemmed |
The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn’s Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions? |
title_sort |
role of genetic factors in characterizing extra-intestinal manifestations in crohn’s disease patients: are bayesian machine learning methods improving outcome predictions? |
publisher |
MDPI AG |
series |
Journal of Clinical Medicine |
issn |
2077-0383 |
publishDate |
2019-06-01 |
description |
(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. |
topic |
Crohn’s disease extra-intestinal manifestation risk prediction Bayesian methods machine learning techniques |
url |
https://www.mdpi.com/2077-0383/8/6/865 |
work_keys_str_mv |
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