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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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Autores principales: Gavrilescu, Otilia, Popa, Iolanda Valentina, Dranga, Mihaela, Mihai, Ruxandra, Cijevschi Prelipcean, Cristina, Mihai, Cătălina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10253703/
https://www.ncbi.nlm.nih.gov/pubmed/37297804
http://dx.doi.org/10.3390/jcm12113609
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author Gavrilescu, Otilia
Popa, Iolanda Valentina
Dranga, Mihaela
Mihai, Ruxandra
Cijevschi Prelipcean, Cristina
Mihai, Cătălina
author_facet Gavrilescu, Otilia
Popa, Iolanda Valentina
Dranga, Mihaela
Mihai, Ruxandra
Cijevschi Prelipcean, Cristina
Mihai, Cătălina
author_sort Gavrilescu, Otilia
collection PubMed
description 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) score and low-cost biological predictors to estimate EDA. Four random forest (RF) and four multilayer perceptron (MLP) classifiers were proposed. The results show that the inclusion of IBDQ in the list of predictors that were fed to the models improved accuracy and the AUC for both the RF and the MLP algorithms. Moreover, the RF technique performed noticeably better than the MLP method on unseen data (the independent patient cohort). This is the first study to propose the use of IBDQ as a predictor in an ML model to estimate UC EDA. The deployment of this ML model can furnish doctors and patients with valuable insights into EDA, a highly beneficial resource for individuals with UC who need long-term treatment.
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spelling pubmed-102537032023-06-10 Laboratory Data and IBDQ—Effective Predictors for the Non-Invasive Machine-Learning-Based Prediction of Endoscopic Activity in Ulcerative Colitis Gavrilescu, Otilia Popa, Iolanda Valentina Dranga, Mihaela Mihai, Ruxandra Cijevschi Prelipcean, Cristina Mihai, Cătălina J Clin Med Article 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) score and low-cost biological predictors to estimate EDA. Four random forest (RF) and four multilayer perceptron (MLP) classifiers were proposed. The results show that the inclusion of IBDQ in the list of predictors that were fed to the models improved accuracy and the AUC for both the RF and the MLP algorithms. Moreover, the RF technique performed noticeably better than the MLP method on unseen data (the independent patient cohort). This is the first study to propose the use of IBDQ as a predictor in an ML model to estimate UC EDA. The deployment of this ML model can furnish doctors and patients with valuable insights into EDA, a highly beneficial resource for individuals with UC who need long-term treatment. MDPI 2023-05-23 /pmc/articles/PMC10253703/ /pubmed/37297804 http://dx.doi.org/10.3390/jcm12113609 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gavrilescu, Otilia
Popa, Iolanda Valentina
Dranga, Mihaela
Mihai, Ruxandra
Cijevschi Prelipcean, Cristina
Mihai, Cătălina
Laboratory Data and IBDQ—Effective Predictors for the Non-Invasive Machine-Learning-Based Prediction of Endoscopic Activity in Ulcerative Colitis
title Laboratory Data and IBDQ—Effective Predictors for the Non-Invasive Machine-Learning-Based Prediction of Endoscopic Activity in Ulcerative Colitis
title_full Laboratory Data and IBDQ—Effective Predictors for the Non-Invasive Machine-Learning-Based Prediction of Endoscopic Activity in Ulcerative Colitis
title_fullStr Laboratory Data and IBDQ—Effective Predictors for the Non-Invasive Machine-Learning-Based Prediction of Endoscopic Activity in Ulcerative Colitis
title_full_unstemmed Laboratory Data and IBDQ—Effective Predictors for the Non-Invasive Machine-Learning-Based Prediction of Endoscopic Activity in Ulcerative Colitis
title_short Laboratory Data and IBDQ—Effective Predictors for the Non-Invasive Machine-Learning-Based Prediction of Endoscopic Activity in Ulcerative Colitis
title_sort laboratory data and ibdq—effective predictors for the non-invasive machine-learning-based prediction of endoscopic activity in ulcerative colitis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10253703/
https://www.ncbi.nlm.nih.gov/pubmed/37297804
http://dx.doi.org/10.3390/jcm12113609
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