ArticleGastroenterology research and practice2020
Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients.
Article in Gastroenterology research and practice, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 4 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Risk prediction models for acute kidney injury in patients with acute pancreatitis: a systematic review.BMC gastroenterology · 2026Pooled 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
- Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- Concentration and predictive value of plasma neutrophil gelatinase-associated lipocalin in patients with acute pancreatitis and acute kidney injury.World journal of nephrology · 2025Article
- Artificial intelligence in pancreatitis: A narrative review on advancing precision diagnosis, prognosis, and therapeutic strategies.World journal of gastroenterology · 2025Review
- Automated machine learning for early prediction of systemic inflammatory response syndrome in acute pancreatitis.BMC medical informatics and decision making · 2025Article
- Article
- Leveraging Large Language Models for Predicting Postoperative Acute Kidney Injury in Elderly Patients.BME frontiers · 2025Article
- Automated machine learning for early prediction of acute kidney injury in acute pancreatitis.BMC medical informatics and decision making · 2024Article
- Prediction of acute kidney injury in patients with liver cirrhosis using machine learning models: evidence from the MIMIC-III and MIMIC-IV.International urology and nephrology · 2024Article
- Development and validation of a prediction model for the early occurrence of acute kidney injury in patients with acute pancreatitis.Renal failure · 2023Article
- Acute pancreatitis: A review of diagnosis, severity prediction and prognosis assessment from imaging technology, scoring system and artificial intelligence.World journal of gastroenterology · 2023Review
- The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review.Biosensors · 2023Review
- To Establish an Early Prediction Model for Acute Respiratory Distress Syndrome in Severe Acute Pancreatitis Using Machine Learning Algorithm.Journal of clinical medicine · 2023Article
- Machine learning in pancreas surgery, what is new? literature review.Frontiers in surgery · 2023Review
- Early prediction of acute kidney injury in patients with gastrointestinal bleeding admitted to the intensive care unit based on extreme gradient boosting.Frontiers in medicine · 2023Article
- Machine learning for acute kidney injury: Changing the traditional disease prediction mode.Frontiers in medicine · 2023Review
- Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost.Frontiers in public health · 2023Article
- Identifying Patients at Risk of Acute Kidney Injury among Patients Receiving Immune Checkpoint Inhibitors: A Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
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.