ArticleRenal failure2020
Prediction models for acute kidney injury in patients with gastrointestinal cancers: a real-world study based on Bayesian networks.
Article in Renal failure, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- Prediction of Acute Kidney Injury in Oncology: From Clinical Risk Scores to AI-Enabled Precision Onco-Nephrology.Cancers · 2026Review
- Case Report: Acute kidney injury due to minimal dilated obstructive nephropathy in the context of gastric cancer.Frontiers in oncology · 2025Article
- The identification and prediction of atrial fibrillation in coronary artery disease patients: a multicentre retrospective study based on Bayesian network.Annals of medicine · 2024Article
- Acute kidney injury after radical gastrectomy: incidence, risk factors, and impact on prognosis.Gastroenterology report · 2024Article
- Diabetes mellitus early warning and factor analysis using ensemble Bayesian networks with SMOTE-ENN and Boruta.Scientific reports · 2023Article
- Poor glycaemic control and ectopic fat deposition mediates the increased risk of non-alcoholic steatohepatitis in high-risk populations with type 2 diabetes: Insights from Bayesian-network modelling.Frontiers in endocrinology · 2023Article
- Machine learning for acute kidney injury: Changing the traditional disease prediction mode.Frontiers in medicine · 2023Review
- Bayesian networks and imaging-derived phenotypes highlight the role of fat deposition in COVID-19 hospitalisation risk.Frontiers in bioinformatics · 2023Article
- Machine learning for the prediction of acute kidney injury in critical care patients with acute cerebrovascular disease.Renal failure · 2022Article
- Machine learning models for predicting acute kidney injury: a systematic review and critical appraisal.Clinical kidney journal · 2022Article
- Prognostic factors for renal function deterioration during palliative first-line chemotherapy for metastatic colorectal cancer: a retrospective study.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2022Article
- Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records.JAMA network open · 2022Article
- Predicting mortality in acute kidney injury patients undergoing continuous renal replacement therapy using a visualization model: A retrospective study.Frontiers in physiology · 2022Article
- Acute kidney injury prevalence in patients with colorectal cancer undergoing surgery with curative intent.Contemporary oncology (Poznan, Poland) · 2022Article
- Management of acute kidney injury in gastrointestinal tumor: An overview.World journal of clinical cases · 2021Review
- Development and Validation of a Prediction Model for Survival in Diabetic Patients With Acute Kidney Injury.Frontiers in endocrinology · 2021Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study attempts to establish a Bayesian networks (BNs) based model for inferring the risk of AKI in gastrointestinal cancer (GI) patients, and to compare its predictive capacity with other machine learning (ML) models.
methodsFrom 1 October 2014 to 30 September 2015, we recruited 6495 inpatients with GI cancers in a tertiary hospital in eastern China. Data on demographics, clinical and laboratory indicators were retrospectively extracted from the electronic medical record system. Predictors of AKI were selected in gLASSO regression, and further incorporated into BNs analysis.
resultsThe incidences of AKI in patients with esophagus, stomach, and intestine cancer were 20.5%, 13.9%, and 12.5%, respectively. Through gLASSO, 11 predictors were screened out, including diabetes, cancer category, anti-tumor treatment, ALT, serum creatinine, estimated glomerular filtration rate (eGFR), serum uric acid (SUA), hypoalbuminemia, anemia, abnormal sodium, and potassium. BNs model revealed that cancer category, treatment, eGFR, and hypoalbuminemia had direct connections with AKI. Diabetes and SUA were indirectly linked to AKI through eGFR, and anemia created connections with AKI through affecting album level. Compared with other ML models, BNs model maintained a higher AUC value in both the internal and external validation (AUC: 0.823/0.790).
conclusionBNs model not only delineates the qualitative and quantitative relationship between AKI and its associated factors but shows the more robust generalizability in AKI prediction.
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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 Socratic graph.