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

Journal of Clinical Medicine

YoungJae Kim¹  SungJin Park¹  KwangGi Kim¹  KangYoon Lee²  DongKyun Park³  YounI Choi³  KyoungOh Kim³  JaeHee Cho³  YoonJae Kim³  Jun-Won Chung³ 

[1] Department of Biomedical Engineering, Gachon University College of Medicine, Incheon 21565, Korea;Department of Computer Engineering, Gachon University, Seongnamdaero, Sugjeong-gu, Seongnam-si, Gyenonggi-do 13306, Korea;Department of Gastroenterology, Gachon University College of Internal Medicine, Gil Medical Center, 405-760 1198 Guwol dong, Namdong-gu, Incheon 21565, Korea;

关键词: machine-learning;    IBD;    UC;    CD;   

DOI  :  10.3390/jcm9113427

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown