ArticleMediators of inflammation2021
Prediction of Multiple Organ Failure Complicated by Moderately Severe or Severe Acute Pancreatitis Based on Machine Learning: A Multicenter Cohort Study.
Article in Mediators of inflammation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 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
- Usefulness of Random Forest Algorithm in Predicting Severe Acute Pancreatitis.Frontiers in cellular and infection microbiology · 2022Trial
- Development and validation of a nomogram for predicting 30-day mortality in patients with severe acute pancreatitis and pneumonia.Scientific reports · 2025Article
- Constructing a prediction model for acute pancreatitis severity based on liquid neural network.Scientific reports · 2025Article
- Automated machine learning for early prediction of systemic inflammatory response syndrome in acute pancreatitis.BMC medical informatics and decision making · 2025Article
- Explainable machine learning model for predicting acute pancreatitis mortality in the intensive care unit.BMC gastroenterology · 2025Article
- Construction and validation of risk prediction models for renal replacement therapy in patients with acute pancreatitis.European journal of medical research · 2025Article
- Development and validation of an explainable machine learning model for mortality prediction among patients with infected pancreatic necrosis.EClinicalMedicine · 2025Article
- A deep learning-powered diagnostic model for acute pancreatitis.BMC medical imaging · 2024Article
- Automated machine learning for early prediction of acute kidney injury in acute pancreatitis.BMC medical informatics and decision making · 2024Article
- Advances in the clinical application of machine learning in acute pancreatitis: a review.Frontiers in medicine · 2024Review
- Machine learning improves prediction of severity and outcomes of acute pancreatitis: a prospective multi-center cohort study.Science China. Life sciences · 2023Article
- Predicting the Need for Therapeutic Intervention and Mortality in Acute Pancreatitis: A Two-Center International Study Using Machine Learning.Journal of personalized medicine · 2022Article
- Automated Machine Learning for the Early Prediction of the Severity of Acute Pancreatitis in Hospitals.Frontiers in cellular and infection microbiology · 2022Article
- Machine Learning Methods for Predicting Long-Term Mortality in Patients After Cardiac Surgery.Frontiers in cardiovascular medicine · 2022Article
- Development and validation of a prediction model for deep vein thrombosis in older non-mild acute pancreatitis patients.World journal of gastrointestinal surgery · 2021Article
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMultiple organ failure (MOF) may lead to an increased mortality rate of moderately severe (MSAP) or severe acute pancreatitis (SAP). This study is aimed to use machine learning to predict the risk of MOF in the course of disease.
methodsClinical and laboratory features with significant differences between patients with and without MOF were screened out by univariate analysis. Prediction models were developed for selected features through six machine learning methods. The models were internally validated with a five-fold cross-validation, and a series of optimal feature subsets were generated in corresponding models. A test set was used to evaluate the predictive performance of the six models.
results305 (68%) of 455 patients with MSAP or SAP developed MOF. Eighteen features with significant differences between the group with MOF and without it in the training and validation set were used for modeling. Interleukin-6 levels, creatinine levels, and the kinetic time were the three most important features in the optimal feature subsets selected by K-fold cross-validation. The adaptive boosting algorithm (AdaBoost) showed the best predictive performance with the highest AUC value (0.826; 95% confidence interval: 0.740 to 0.888). The sensitivity of AdaBoost (80.49%) and specificity of logistic regression analysis (93.33%) were the best scores among the six models in the test set.
conclusionsA predictive model of MOF complicated by MSAP or SAP was successfully developed based on machine learning. The predictive performance was evaluated by a test set, for which AdaBoost showed a satisfactory predictive performance. The study is registered with the China Clinical Trial Registry (Identifier: ChiCTR1800016079).
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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.