ArticleBMC gastroenterology2019
Development and validation of three machine-learning models for predicting multiple organ failure in moderately severe and severe acute pancreatitis.
Article in BMC gastroenterology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04735055 (Artificial Intelligence Application in Predicting Disease Severity in Acute Pancreatitis), which is not on this map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Artificial Intelligence Application in Predicting Disease Severity in Acute Pancreatitis
Who cites it
24 citing papers in PubMed, 67 citations in OpenAlex.
- Construction and validation of machine learning models for sepsis prediction in patients with acute pancreatitis.BMC surgery · 2023Trial
- Using machine learning models to predict acute pancreatitis in children with pancreaticobiliary maljunction.Surgery today · 2023Trial
- Development and validation of a risk prediction score for the severity of acute hypertriglyceridemic pancreatitis in Chinese patients.World journal of gastroenterology · 2022Trial
- Artificial intelligence in pancreatitis: A narrative review on advancing precision diagnosis, prognosis, and therapeutic strategies.World journal of gastroenterology · 2025Review
- AI and Machine Learning for Precision Medicine in Acute Pancreatitis: A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- Explainable machine learning model for predicting acute pancreatitis mortality in the intensive care unit.BMC gastroenterology · 2025Article
- Interpretable prediction of 30-day mortality in patients with acute pancreatitis based on machine learning and SHAP.BMC medical informatics and decision making · 2024Article
- Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives.Therapeutic advances in vaccines and immunotherapy · 2024Review
- Advances in the clinical application of machine learning in acute pancreatitis: a review.Frontiers in medicine · 2024Review
- A Machine Learning Approach Using [Molecular imaging and biology · 2023Article
- Prediction of post-operative acute pancreatitis in children with pancreaticobiliary maljunction using machine learning model.Pediatric surgery international · 2023Article
- Machine learning model identifies aggressive acute pancreatitis within 48 h of admission: a large retrospective study.BMC medical informatics and decision making · 2022Article
- How Does Government Efficiency Affect Health Outcomes? The Empirical Evidence from 156 Countries.International journal of environmental research and public health · 2022Article
- EASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis.Clinical and translational medicine · 2022Observational
- Artificial intelligence: Emerging player in the diagnosis and treatment of digestive disease.World journal of gastroenterology · 2022Review
- Predicting the Need for Therapeutic Intervention and Mortality in Acute Pancreatitis: A Two-Center International Study Using Machine Learning.Journal of personalized medicine · 2022Article
- Comparative Analysis of Early Clinical Features and Complications of Different Types of Acute Pancreatitis.Oxidative medicine and cellular longevity · 2022Article
- Automated Machine Learning for the Early Prediction of the Severity of Acute Pancreatitis in Hospitals.Frontiers in cellular and infection microbiology · 2022Article
- Artificial intelligence in gastroenterology: A state-of-the-art review.World journal of gastroenterology · 2021Review
- An Artificial Neural Networks Model for Early Predicting In-Hospital Mortality in Acute Pancreatitis in MIMIC-III.BioMed research international · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundMultiple organ failure (MOF) is a serious complication of moderately severe (MASP) and severe acute pancreatitis (SAP). This study aimed to develop and assess three machine-learning models to predict MOF.
methodsPatients with MSAP and SAP who were admitted from July 2014 to June 2017 were included. Firstly, parameters with significant differences between patients with MOF and without MOF were screened out by univariate analysis. Then, support vector machine (SVM), logistic regression analysis (LRA) and artificial neural networks (ANN) models were constructed based on these factors, and five-fold cross-validation was used to train each model.
resultsA total of 263 patients were enrolled. Univariate analysis screened out sixteen parameters referring to blood volume, inflammatory, coagulation and renal function to construct machine-learning models. The predictive efficiency of the optimal combinations of features by SVM, LRA, and ANN was almost equal (AUC = 0.840, 0.832, and 0.834, respectively), as well as the Acute Physiology and Chronic Health Evaluation II score (AUC = 0.814, P > 0.05). The common important predictive factors were HCT, K-time, IL-6 and creatinine in three models.
conclusionsThree machine-learning models can be efficient prognostic tools for predicting MOF in MSAP and SAP. ANN is recommended, which only needs four common parameters.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.