2021
DOI: 10.3389/fmed.2021.666190
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Machine Learning Classification of Inflammatory Bowel Disease in Children Based on a Large Real-World Pediatric Cohort CEDATA-GPGE® Registry

Abstract: Introduction: The rising incidence of pediatric inflammatory bowel diseases (PIBD) facilitates the need for new methods of improving diagnosis latency, quality of care and documentation. Machine learning models have shown to be applicable to classifying PIBD when using histological data or extensive serology. This study aims to evaluate the performance of algorithms based on promptly available data more suited to clinical applications.Methods: Data of inflammatory locations of the bowels from initial and follo… Show more

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Cited by 3 publications
(3 citation statements)
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“…This might have influenced the results, especially regarding the high feature importance. Schneider et al recently reported the added value of including laboratory parameters for machine learning based classification of inflammatory bowel disease in children (9). The high feature importance of inflammatory markers suggests that adding additional laboratory data such as antibody status could improve the accuracy of Rheport.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…This might have influenced the results, especially regarding the high feature importance. Schneider et al recently reported the added value of including laboratory parameters for machine learning based classification of inflammatory bowel disease in children (9). The high feature importance of inflammatory markers suggests that adding additional laboratory data such as antibody status could improve the accuracy of Rheport.…”
Section: Discussionmentioning
confidence: 99%
“…Recent studies showed that Rheport was well accepted and perceived as easy to use by patients (8), however, the diagnostic accuracy was limited (4). Machine learning has been successfully used in various disciplines to increase diagnostic accuracy (9)(10)(11). In rheumatology, expert-level performance has recently been achieved using deep learning for detection of radiographic sacroiliitis (12) and individual risk of disease flares could be predicted in patients with rheumatoid arthritis using advanced machine learning (10).…”
Section: Introductionmentioning
confidence: 99%
“…Besides omics, different clinical measurements used for IBD diagnosis and tracking of the disease status, such as fecal calprotectin, blood parameters, serum C-reactive protein, endoscopic and/or medical imaging, possess a large potential that could be exploited in machine learning modeling. A number of studies analyzed usage of clinically valuable traits in IBD diagnostic, prognostic and therapeutic outcome predictions [ 40 , 41 , 42 , 43 , 44 , 45 ]. For instance, it has been demonstrated that machine learning algorithms employing laboratory and age data outperformed drug metabolic measurements in predicting the response of IBD patients to thiopurines [ 41 ].…”
Section: Introductionmentioning
confidence: 99%