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
DC Field | Value | Language |
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dc.contributor.author | Choi, Youn I | - |
dc.contributor.author | Park, Sung Jin | - |
dc.contributor.author | Chung, Jun-Won | - |
dc.contributor.author | Kim, Kyoung Oh | - |
dc.contributor.author | Cho, Jae Hee | - |
dc.contributor.author | Kim, Young Jae | - |
dc.contributor.author | Lee, Kang Yoon | - |
dc.contributor.author | Kim, Kwang Gi | - |
dc.contributor.author | Park, Dong Kyun | - |
dc.contributor.author | Kim, Yoon Jae | - |
dc.date.available | 2020-12-14T00:40:38Z | - |
dc.date.created | 2020-12-14 | - |
dc.date.issued | 2020-11 | - |
dc.identifier.issn | 2077-0383 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/79236 | - |
dc.description.abstract | 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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | JOURNAL OF CLINICAL MEDICINE | - |
dc.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 | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000593361300001 | - |
dc.identifier.doi | 10.3390/jcm9113427 | - |
dc.identifier.bibliographicCitation | JOURNAL OF CLINICAL MEDICINE, v.9, no.11 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85106436147 | - |
dc.citation.title | JOURNAL OF CLINICAL MEDICINE | - |
dc.citation.volume | 9 | - |
dc.citation.number | 11 | - |
dc.contributor.affiliatedAuthor | Choi, Youn I | - |
dc.contributor.affiliatedAuthor | Park, Sung Jin | - |
dc.contributor.affiliatedAuthor | Chung, Jun-Won | - |
dc.contributor.affiliatedAuthor | Kim, Kyoung Oh | - |
dc.contributor.affiliatedAuthor | Cho, Jae Hee | - |
dc.contributor.affiliatedAuthor | Kim, Young Jae | - |
dc.contributor.affiliatedAuthor | Lee, Kang Yoon | - |
dc.contributor.affiliatedAuthor | Kim, Kwang Gi | - |
dc.contributor.affiliatedAuthor | Park, Dong Kyun | - |
dc.contributor.affiliatedAuthor | Kim, Yoon Jae | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | machine-learning | - |
dc.subject.keywordAuthor | IBD | - |
dc.subject.keywordAuthor | UC | - |
dc.subject.keywordAuthor | CD | - |
dc.subject.keywordPlus | TOP-DOWN | - |
dc.subject.keywordPlus | STEP-UP | - |
dc.subject.keywordPlus | PREVALENCE | - |
dc.subject.keywordPlus | BURDEN | - |
dc.subject.keywordPlus | COHORT | - |
dc.subject.keywordPlus | CROHNS | - |
dc.relation.journalResearchArea | General & Internal Medicine | - |
dc.relation.journalWebOfScienceCategory | Medicine, General & Internal | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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