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
- Authors
- 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
- Issue Date
- Nov-2020
- Publisher
- MDPI
- Keywords
- machine-learning; IBD; UC; CD
- Citation
- JOURNAL OF CLINICAL MEDICINE, v.9, no.11
- Journal Title
- JOURNAL OF CLINICAL MEDICINE
- Volume
- 9
- Number
- 11
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/79236
- DOI
- 10.3390/jcm9113427
- ISSN
- 2077-0383
- 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.
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Collections - IT융합대학 > 컴퓨터공학과 > 1. Journal Articles
- 보건과학대학 > 의용생체공학과 > 1. Journal Articles
- 의과대학 > 의학과 > 1. Journal Articles
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