ArticleInternational journal of general medicine2022
Machine Learning-Assisted Ensemble Analysis for the Prediction of Acute Pancreatitis with Acute Kidney Injury.
Article in International journal of general medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 4 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Risk prediction models for acute kidney injury in patients with acute pancreatitis: a systematic review.BMC gastroenterology · 2026Pooled it
- A systematic review of machine learning-based prognostic models for acute pancreatitis: Towards improving methods and reporting quality.PLoS medicine · 2025Pooled it
- Predictive models for acute kidney injury in acute pancreatitis: a systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Exploring the role of Artificial Intelligence in Acute Kidney Injury management: a comprehensive review and future research agenda.BMC medical informatics and decision making · 2024Pooled it
- Construction and validation of machine learning models for sepsis prediction in patients with acute pancreatitis.BMC surgery · 2023Trial
- Interpretable machine learning reveals phosphorus-to-albumin ratio as a novel predictor of mortality and acute kidney injury in critically ill pancreatitis patients: a multi-center retrospective analysis.European journal of medical research · 2026Article
- Novel graph-based centralized and decentralized approaches for early AKI prediction.Scientific reports · 2025Article
- Construction and validation of risk prediction models for renal replacement therapy in patients with acute pancreatitis.European journal of medical research · 2025Article
- Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database.Renal failure · 2024Article
- Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions.The Korean journal of internal medicine · 2024Review
- Automated machine learning for early prediction of acute kidney injury in acute pancreatitis.BMC medical informatics and decision making · 2024Article
- Establishment and Validation of an Early Predictive Model for Severe Acute Pancreatitis.Journal of inflammation research · 2024Article
- Study on the Predictive Value of Renal Resistive Index Combined with β2-Microglobulin in Patients with Urosepsis Complicated with Acute Kidney Injury.Journal of inflammation research · 2024Article
- Bibliometric and visual analysis of machine learning-based research in acute kidney injury worldwide.Frontiers in public health · 2023Article
- Machine learning for acute kidney injury: Changing the traditional disease prediction mode.Frontiers in medicine · 2023Review
- Prediction of acute pancreatitis complications using routine blood parameters during early admission.Immunity, inflammation and disease · 2022Article
- Interpretable machine learning for early prediction of acute kidney injury in critically ill patients with acute pancreatitis.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Acute kidney injury (AKI) is a frequent complication of severe acute pancreatitis (AP) and carries a very poor prognosis. The present study aimed to construct a model capable of accurately identifying those patients at high risk of harboring occult acute kidney injury (AKI) characteristics. Patients and Methods: We retrospectively recruited a total of 424 consecutive patients at the Gezhouba central hospital of Sinopharm and Xianning central hospital between January 1, 2016, and October 30, 2021. ML-assisted models were developed from candidate clinical features using two-step estimation methods. The receiver operating characteristic curve (ROC), decision curve analysis (DCA), and clinical impact curve (CIC) were performed to evaluate the robustness and clinical practicability of each model. Results: Finally, a total of 30 candidate variables were included, and the AKI prediction model was established by an ML-based algorithm. The areas under the ROC curve (AUCs) of the random forest classifier (RFC) model, support vector machine (SVM), eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and decision tree (DT) ranged from 0.725 (95% CI 0.223-1.227) to 0.902 (95% CI 0.400-1.403). Among them, RFC obtained the optimal prediction efficiency via adding inflammatory factors, which are serum creatinine (Scr), C-reactive protein (CRP), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-albumin ratio (NAR), and CysC, respectively. Conclusion: We successfully developed ML-based prediction models for AKI, particularly the RFC, which can improve the prediction of AKI in patients with AP. The practicality of prediction and early detection may be greatly beneficial to risk stratification and management decisions.
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