ArticleScientific reports2022
Early prediction of acute necrotizing pancreatitis by artificial intelligence: a prospective cohort-analysis of 2387 cases.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- A systematic review of machine learning-based prognostic models for acute pancreatitis: Towards improving methods and reporting quality.PLoS medicine · 2025Pooled it
- Metabolic syndrome components individually worsen the outcome of acute pancreatitis: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Persistently High Procalcitonin and C-Reactive Protein Are Good Predictors of Infection in Acute Necrotizing Pancreatitis: A Systematic Review and Meta-Analysis.International journal of molecular sciences · 2024Pooled it
- Artificial Intelligence in Veterinary Clinical Pathology-An Introduction and Review.Veterinary clinical pathology · 2025Review
- Article
- Implementation and Updating of Clinical Prediction Models: A Systematic Review.Mayo Clinic proceedings. Digital health · 2025Review
- Evaluating the Role of Artificial Intelligence in Making Clinical Decisions for Treating Acute Pancreatitis.Journal of clinical medicine · 2025Article
- Application of deep learning models for accurate classification of fluid collections in acute necrotizing pancreatitis on computed tomography: a multicenter study.Abdominal radiology (New York) · 2025Article
- AI and Machine Learning for Precision Medicine in Acute Pancreatitis: A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- The 12-Year Experience of the Hungarian Pancreatic Study Group.Journal of clinical medicine · 2025Review
- Development of a Nomogram to Predict the Risk for Acute Necrotizing Pancreatitis.Gut and liver · 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
- Advances in the clinical application of machine learning in acute pancreatitis: a review.Frontiers in medicine · 2024Review
- 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 improves prediction of severity and outcomes of acute pancreatitis: a prospective multi-center cohort study.Science China. Life sciences · 2023Article
- Acute Pancreatitis Severity Prediction: It Is Time to Use Artificial Intelligence.Journal of clinical medicine · 2022Article
- Stmol: A component for building interactive molecular visualizations within streamlit web-applications.Frontiers in molecular biosciences · 2022Article
- Gut microbiota on admission as predictive biomarker for acute necrotizing pancreatitis.Frontiers in immunology · 2022Article
Corrections and comments
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Authors and funding
35 authors at 15 institutions in 8 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pancreatic necrosis is a consistent prognostic factor in acute pancreatitis (AP). However, the clinical scores currently in use are either too complicated or require data that are unavailable on admission or lack sufficient predictive value. We therefore aimed to develop a tool to aid in necrosis prediction. The XGBoost machine learning algorithm processed data from 2387 patients with AP. The confidence of the model was estimated by a bootstrapping method and interpreted via the 10th and the 90th percentiles of the prediction scores. Shapley Additive exPlanations (SHAP) values were calculated to quantify the contribution of each variable provided. Finally, the model was implemented as an online application using the Streamlit Python-based framework. The XGBoost classifier provided an AUC value of 0.757. Glucose, C-reactive protein, alkaline phosphatase, gender and total white blood cell count have the most impact on prediction based on the SHAP values. The relationship between the size of the training dataset and model performance shows that prediction performance can be improved. This study combines necrosis prediction and artificial intelligence. The predictive potential of this model is comparable to the current clinical scoring systems and has several advantages over them.
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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.