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Development of Machine Learning Model to Predict the 5-Year Risk of Starting Biologic Agents in Patients with Inflammatory Bowel Disease (IBD): K-CDM Network Study
Background: The incidence and global burden of inflammatory bowel disease (IBD) have steadily increased in the past few decades. Improved methods to stratify risk and predict disease-related outcomes are required for IBD. Aim: The aim of this study was to develop and validate a machine learning (ML)...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7693158/ https://www.ncbi.nlm.nih.gov/pubmed/33114505 http://dx.doi.org/10.3390/jcm9113427 |
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author | Choi, Youn I Park, Sung Jin Chung, Jun-Won Kim, Kyoung Oh Cho, Jae Hee Kim, Young Jae Lee, Kang Yoon Kim, Kwang Gi Park, Dong Kyun Kim, Yoon Jae |
author_facet | Choi, Youn I Park, Sung Jin Chung, Jun-Won Kim, Kyoung Oh Cho, Jae Hee Kim, Young Jae Lee, Kang Yoon Kim, Kwang Gi Park, Dong Kyun Kim, Yoon Jae |
author_sort | Choi, Youn I |
collection | PubMed |
description | Background: The incidence and global burden of inflammatory bowel disease (IBD) have steadily increased in the past few decades. Improved methods to stratify risk and predict disease-related outcomes are required for IBD. Aim: The aim of this study was to develop and validate a machine learning (ML) model to predict the 5-year risk of starting biologic agents in IBD patients. Method: We applied an ML method to the database of the Korean common data model (K-CDM) network, a data sharing consortium of tertiary centers in Korea, to develop a model to predict the 5-year risk of starting biologic agents in IBD patients. The records analyzed were those of patients diagnosed with IBD between January 2006 and June 2017 at Gil Medical Center (GMC; n = 1299) or present in the K-CDM network (n = 3286). The ML algorithm was developed to predict 5- year risk of starting biologic agents in IBD patients using data from GMC and externally validated with the K-CDM network database. Result: The ML model for prediction of IBD-related outcomes at 5 years after diagnosis yielded an area under the curve (AUC) of 0.86 (95% CI: 0.82–0.92), in an internal validation study carried out at GMC. The model performed consistently across a range of other datasets, including that of the K-CDM network (AUC = 0.81; 95% CI: 0.80–0.85), in an external validation study. Conclusion: The ML-based prediction model can be used to identify IBD-related outcomes in patients at risk, enabling physicians to perform close follow-up based on the patient’s risk level, estimated through the ML algorithm. |
format | Online Article Text |
id | pubmed-7693158 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76931582020-11-28 Development of Machine Learning Model to Predict the 5-Year Risk of Starting Biologic Agents in Patients with Inflammatory Bowel Disease (IBD): K-CDM Network Study Choi, Youn I Park, Sung Jin Chung, Jun-Won Kim, Kyoung Oh Cho, Jae Hee Kim, Young Jae Lee, Kang Yoon Kim, Kwang Gi Park, Dong Kyun Kim, Yoon Jae J Clin Med Article Background: The incidence and global burden of inflammatory bowel disease (IBD) have steadily increased in the past few decades. Improved methods to stratify risk and predict disease-related outcomes are required for IBD. Aim: The aim of this study was to develop and validate a machine learning (ML) model to predict the 5-year risk of starting biologic agents in IBD patients. Method: We applied an ML method to the database of the Korean common data model (K-CDM) network, a data sharing consortium of tertiary centers in Korea, to develop a model to predict the 5-year risk of starting biologic agents in IBD patients. The records analyzed were those of patients diagnosed with IBD between January 2006 and June 2017 at Gil Medical Center (GMC; n = 1299) or present in the K-CDM network (n = 3286). The ML algorithm was developed to predict 5- year risk of starting biologic agents in IBD patients using data from GMC and externally validated with the K-CDM network database. Result: The ML model for prediction of IBD-related outcomes at 5 years after diagnosis yielded an area under the curve (AUC) of 0.86 (95% CI: 0.82–0.92), in an internal validation study carried out at GMC. The model performed consistently across a range of other datasets, including that of the K-CDM network (AUC = 0.81; 95% CI: 0.80–0.85), in an external validation study. Conclusion: The ML-based prediction model can be used to identify IBD-related outcomes in patients at risk, enabling physicians to perform close follow-up based on the patient’s risk level, estimated through the ML algorithm. MDPI 2020-10-26 /pmc/articles/PMC7693158/ /pubmed/33114505 http://dx.doi.org/10.3390/jcm9113427 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Choi, Youn I Park, Sung Jin Chung, Jun-Won Kim, Kyoung Oh Cho, Jae Hee Kim, Young Jae Lee, Kang Yoon Kim, Kwang Gi Park, Dong Kyun Kim, Yoon Jae Development of Machine Learning Model to Predict the 5-Year Risk of Starting Biologic Agents in Patients with Inflammatory Bowel Disease (IBD): K-CDM Network Study |
title | Development of Machine Learning Model to Predict the 5-Year Risk of Starting Biologic Agents in Patients with Inflammatory Bowel Disease (IBD): K-CDM Network Study |
title_full | Development of Machine Learning Model to Predict the 5-Year Risk of Starting Biologic Agents in Patients with Inflammatory Bowel Disease (IBD): K-CDM Network Study |
title_fullStr | Development of Machine Learning Model to Predict the 5-Year Risk of Starting Biologic Agents in Patients with Inflammatory Bowel Disease (IBD): K-CDM Network Study |
title_full_unstemmed | Development of Machine Learning Model to Predict the 5-Year Risk of Starting Biologic Agents in Patients with Inflammatory Bowel Disease (IBD): K-CDM Network Study |
title_short | Development of Machine Learning Model to Predict the 5-Year Risk of Starting Biologic Agents in Patients with Inflammatory Bowel Disease (IBD): K-CDM Network Study |
title_sort | development of machine learning model to predict the 5-year risk of starting biologic agents in patients with inflammatory bowel disease (ibd): k-cdm network study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7693158/ https://www.ncbi.nlm.nih.gov/pubmed/33114505 http://dx.doi.org/10.3390/jcm9113427 |
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