Laboratory Data and IBDQ—Effective Predictors for the Non-Invasive Machine-Learning-Based Prediction of Endoscopic Activity in Ulcerative Colitis

Laboratory Data and IBDQ—Effective Predictors for the Non-Invasive Machine-Learning-Based Prediction of Endoscopic Activity in Ulcerative Colitis

A suitable, non-invasive biomarker for assessing endoscopic disease activity (EDA) in ulcerative colitis (UC) has yet to be identified. Our study aimed to develop a cost-effective and non-invasive machine learning (ML) method that utilizes the cost-free Inflammatory Bowel Disease Questionnaire (IBDQ...

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Bibliographic Details
Main Authors: Otilia Gavrilescu, Iolanda Valentina Popa, Mihaela Dranga, Ruxandra Mihai, Cristina Cijevschi Prelipcean, Cătălina Mihai
Format: Article
Language:English
Published: MDPI AG 2023-05-01
Series:Journal of Clinical Medicine
Subjects:
ulcerative colitis
disease activity
non-invasive biomarkers
quality of life
machine learning
Online Access:https://www.mdpi.com/2077-0383/12/11/3609

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https://www.mdpi.com/2077-0383/12/11/3609

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