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 follow-up visitations is extracted from the CEDATA-GPGE registry and two follow-up sets are split off containing only input from 2017 and 2018. Pre-processing excludes patients in remission and encodes the categorical data numerically. For classification of PIBD diagnosis, a support vector machine (SVM), a random forest algorithm (RF), extreme gradient boosting (XGBoost), a dense neural network (DNN) and a convolutional neural network (CNN) are employed. As best performer, a convolutional neural network is further improved using grid optimization.Results: The achieved accuracy of the optimized neural network reaches up to 90.57% on data inserted into the registry in 2018. Less performant methods reach 88.78% for the DNN down to 83.94% for the XGBoost. The accuracy of prediction for the 2018 follow-up dataset is higher than those for older datasets. Neural networks yield a higher standard deviation with 3.45 for the CNN compared to 0.83–0.86 of the support vector machine and ensemble methods.Discussion: The displayed accuracy of the convolutional neural network proofs the viability of machine learning classification in PIBD diagnostics using only timely available data.

Machine learning-based improvement of an online rheumatology referral and triage system

“…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.…”

“…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).…”

Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications

“…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 ].…”