ArticleJournal of clinical medicine2019
The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn's Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions?
Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.Inflammatory bowel diseases · 2022Pooled it
- Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review.Journal of Crohn's & colitis · 2022Pooled it
- Prediction of Extraintestinal Manifestations in Inflammatory Bowel Disease Using Clinical and Genetic Variables with Machine Learning in a Latin IBD Group.International journal of molecular sciences · 2025Article
- Development and validation of a risk prediction tool for the diagnosis of inflammatory bowel disease in patients presenting in primary care with abdominal symptoms.Journal of Crohn's & colitis · 2025Article
- Digital biomarkers and artificial intelligence: a new frontier in personalized management of inflammatory bowel disease.Frontiers in immunology · 2025Review
- Computed tomography enterography radiomics and machine learning for identification of Crohn's disease.BMC medical imaging · 2024Article
- Advances in Inflammatory Bowel Disease Diagnostics: Machine Learning and Genomic Profiling Reveal Key Biomarkers for Early Detection.Diagnostics (Basel, Switzerland) · 2024Article
- New pattern of individualized management of chronic diseases: focusing on inflammatory bowel diseases and looking to the future.Frontiers in medicine · 2023Review
- Prediction of treatment outcome in clinical trials under a personalized medicine perspective.Scientific reports · 2022Article
- Artificial intelligence in small intestinal diseases: Application and prospects.World journal of gastroenterology · 2021Review
- Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions.World journal of gastroenterology · 2021Review
- Clinical applications of artificial intelligence and machine learning-based methods in inflammatory bowel disease.Journal of gastroenterology and hepatology · 2021Article
- Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease.Frontiers in bioengineering and biotechnology · 2021Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(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.
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Registered trials
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