ArticleComputational and mathematical methods in medicine2022
A Novel Radiomics-Based Machine Learning Framework for Prediction of Acute Kidney Injury-Related Delirium in Patients Who Underwent Cardiovascular Surgery.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 16 citations in OpenAlex.
- Artificial intelligence in contrast-induced nephropathy after coronary interventions: A meta-analysis.Medicine · 2026Pooled 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
- Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.BMC medical informatics and decision making · 2026Article
- Fusion of clinical magnet resonance images and electronic health records promotes multimodal predictions of postoperative delirium.Scientific reports · 2025Article
- Acute kidney injury and postoperative delirium.Journal of biomedical research · 2025Article
- Shaping the future of cardiac interventions and cardiac surgeries: The impact of virtual reality and artificial intelligence.Global cardiology science & practice · 2025Review
- Prediction Models for Postoperative Delirium of Cardiovascular Surgery (PODOCVS): Protocol for a Systematic Review.JMIR research protocols · 2025Article
- Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review.JAMIA open · 2025Review
- Machine Learning Multimodal Model for Delirium Risk Stratification.JAMA network open · 2025Article
- Clinical decision-making in bone cancer care management and forecast of ICU needs based on computed tomography.Journal of bone oncology · 2024Article
- Development of postoperative delirium prediction models in patients undergoing cardiovascular surgery using machine learning algorithms.Scientific reports · 2023Article
- Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models.BMJ health & care informatics · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute kidney injury (AKI) can be caused by multiple etiologies and is characterized by a sudden and severe decrease in kidney function. Understanding the independent risk factors associated with the development of AKI and its early detection can refine the risk management and clinical decision-making of high-risk patients after cardiovascular surgery. A retrospective analysis was performed in a single teaching hospital between December 1, 2019, and December 31, 2020. The diagnostic performance of novel biomarkers was assessed using random forest, support vector machine, and multivariate logistic regression. The nomogram from multivariate analysis of risk factors associated with AKI indicated that only LVEF, red blood cell input, and ICUmvat contribute to AKI differentiation and that the difference is statistically significant (
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Registered trials
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