Trial reportSurgery today2023
Using machine learning models to predict acute pancreatitis in children with pancreaticobiliary maljunction.
Trial report in Surgery today, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 12 citations in OpenAlex.
- Advances in Minimally Invasive Diagnosis and Treatment Procedures for Pancreaticobiliary Maljunction: A Narrative Review.Digestive diseases and sciences · 2026Review
- Machine learning-based prediction model for risk factors associated with conversion to open surgery in pediatric laparoscopic radical resection of choledochal cysts.Pediatric surgery international · 2026Article
- Detecting pancreaticobiliary maljunction in pediatric congenital choledochal malformation patients using machine learning methods.BMC surgery · 2025Article
- Advancements in the diagnosis of biliopancreatic diseases: A comparative review and study on future insights.World journal of gastrointestinal endoscopy · 2025Review
- The effectiveness of ERCP in managing preoperative complications of choledochal cysts in children and its role in facilitating early surgical intervention.Frontiers in pediatrics · 2025Article
- Development of a diagnostic model for biliary atresia based on MMP7 and serological tests using machine learning.Pediatric surgery international · 2024Article
- Development of a CT-based radiomics-clinical model to diagnose acute pancreatitis on nonobvious findings on CT in children with pancreaticobiliary maljunction.The British journal of radiology · 2024Article
- Construction of nomogram based on clinical factors for the risk prediction of postoperative complications in children with choledochal cyst.Frontiers in pediatrics · 2024Article
- Using machine learning models to predict the surgical risk of children with pancreaticobiliary maljunction and biliary dilatation.Surgery today · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
purposeTo develop a model to identify risk factors and predictors of acute pancreatitis in children with pancreaticobiliary maljunction (PBM).
methodsWe screened consecutive PBM patients treated at two centers between January, 2015 and July, 2021. For machine learning, the cohort was divided randomly at a 6:4 ratio to a training dataset and a validation dataset. Three parallel models were developed using logistic regression (LR), a support vector machine (SVM), and extreme gradient boosting (XGBoost), respectively. Model performance was judged primarily based on the area under the receiver operating curves (AUC).
resultsA total of 99 patients were included in the analysis, 17 of whom suffered acute pancreatitis and 82 did not. The XGBoost (AUC = 0.814) and SVM (AUC = 0.813) models produced similar performance in the validation dataset; both outperformed the LR model (AUC = 0.805). Based on the SHapley Additive exPlanation values, the most important variable in both the XGBoost and SVM models were age, protein plugs, and white blood cell count.
conclusionsMachine learning models, especially XGBoost and SVM, could be used to predict acute pancreatitis in children with PBM. The most important contributing factor to the models were age, protein plugs, and white blood cell count.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.