ArticleFrontiers in cellular and infection microbiology2022
Automated Machine Learning for the Early Prediction of the Severity of Acute Pancreatitis in Hospitals.
Article in Frontiers in cellular and infection microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of the predictive factors for the recurrence of acute pancreatitis.World journal of emergency surgery : WJES · 2025Pooled it
- A systematic review of machine learning-based prognostic models for acute pancreatitis: Towards improving methods and reporting quality.PLoS medicine · 2025Pooled it
- Construction and validation of machine learning models for sepsis prediction in patients with acute pancreatitis.BMC surgery · 2023Trial
- Review
- Acute pancreatitis: mechanisms and therapeutic approaches.Signal transduction and targeted therapy · 2026Review
- Multimodal prediction models integrating radiomics and three-dimensional deep learning for acute respiratory distress syndrome in acute pancreatitis patients.The Journal of international medical research · 2026Article
- Lactate-related biomarkers in severe acute pancreatitis: insights from machine learning, molecular dynamics, and experimental validation.Journal of biological engineering · 2025Article
- Integrating Feature Selection, Machine Learning, and SHAP Explainability to Predict Severe Acute Pancreatitis.Diagnostics (Basel, Switzerland) · 2025Article
- Multimodal predictive model for strangulation risk in adhesive small bowel obstruction using deep learning and electronic health record data.The Journal of international medical research · 2025Observational
- Constructing a prediction model for acute pancreatitis severity based on liquid neural network.Scientific reports · 2025Article
- Using machine learning model for predicting risk of memory decline: A cross sectional study.Heliyon · 2024Article
- Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis.Heliyon · 2024Article
- Automated machine learning for early prediction of acute kidney injury in acute pancreatitis.BMC medical informatics and decision making · 2024Article
- Explainable coronary artery disease prediction model based on AutoGluon from AutoML framework.Frontiers in cardiovascular medicine · 2024Article
- Predicting the risk of early intensive care unit admission for patients hospitalized with acute pancreatitis using supervised machine learning.Proceedings (Baylor University. Medical Center) · 2024Article
- Acute pancreatitis: A review of diagnosis, severity prediction and prognosis assessment from imaging technology, scoring system and artificial intelligence.World journal of gastroenterology · 2023Review
- Machine learning-based decision tool for selecting patients with idiopathic acute pancreatitis for endosonography to exclude a biliary aetiology.World journal of gastroenterology · 2023Article
- Improved outcome prediction in acute pancreatitis with generated data and advanced machine learning algorithms.Turkish journal of emergency medicineArticle
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Machine learning (ML) algorithms are widely applied in building models of medicine due to their powerful studying and generalizing ability. This study aims to explore different ML models for early identification of severe acute pancreatitis (SAP) among patients hospitalized for acute pancreatitis. Methods: This retrospective study enrolled patients with acute pancreatitis (AP) from multiple centers. Data from the First Affiliated Hospital and Changshu No. 1 Hospital of Soochow University were adopted for training and internal validation, and data from the Second Affiliated Hospital of Soochow University were adopted for external validation from January 2017 to December 2021. The diagnosis of AP and SAP was based on the 2012 revised Atlanta classification of acute pancreatitis. Models were built using traditional logistic regression (LR) and automated machine learning (AutoML) analysis with five types of algorithms. The performance of models was evaluated by the receiver operating characteristic (ROC) curve, the calibration curve, and the decision curve analysis (DCA) based on LR and feature importance, SHapley Additive exPlanation (SHAP) Plot, and Local Interpretable Model Agnostic Explanation (LIME) based on AutoML. Results: A total of 1,012 patients were included in this study to develop the AutoML models in the training/validation dataset. An independent dataset of 212 patients was used to test the models. The model developed by the gradient boost machine (GBM) outperformed other models with an area under the ROC curve (AUC) of 0.937 in the validation set and an AUC of 0.945 in the test set. Furthermore, the GBM model achieved the highest sensitivity value of 0.583 among these AutoML models. The model developed by eXtreme Gradient Boosting (XGBoost) achieved the highest specificity value of 0.980 and the highest accuracy of 0.958 in the test set. Conclusions: The AutoML model based on the GBM algorithm for early prediction of SAP showed evident clinical practicability.
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