ArticleJournal of clinical medicine2020
Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches.
Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- Machine learning for the prediction of acute kidney injury post cardiac surgery: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Biomarkers of acute kidney injury after pediatric cardiac surgery: a meta-analysis of diagnostic test accuracy.European journal of pediatrics · 2022Pooled it
- A machine learning-based model for predicting the postoperative risk of acute kidney injury in neonates.Translational pediatrics · 2025Article
- Prediction Model of Intradialytic Hypertension in Hemodialysis Patients Based on Machine Learning.Journal of medical systems · 2025Article
- Deep learning-based prediction model of acute kidney injury following coronary artery bypass grafting in coronary heart disease patients: a multicenter clinical study from China.Frontiers in cardiovascular medicine · 2025Article
- Exploring the Potential of Chatbots in Critical Care Nephrology.Medicines (Basel, Switzerland) · 2023Review
- Ethical Implications of Chatbot Utilization in Nephrology.Journal of personalized medicine · 2023Review
- Characteristics of Kidney Transplant Recipients with Prolonged Pre-Transplant Dialysis Duration as Identified by Machine Learning Consensus Clustering: Pathway to Personalized Care.Journal of personalized medicine · 2023Article
- Development, External Validation, and Visualization of Machine Learning Models for Predicting Occurrence of Acute Kidney Injury after Cardiac Surgery.Reviews in cardiovascular medicine · 2023Article
- Development and validation of a nomogram for blood transfusion during intracranial aneurysm clamping surgery: a retrospective analysis.BMC medical informatics and decision making · 2023Article
- Development and Validation of a Machine Learning Predictive Model for Cardiac Surgery-Associated Acute Kidney Injury.Journal of clinical medicine · 2023Article
- Machine Learning-Based Prediction of Acute Kidney Injury Following Pediatric Cardiac Surgery: Model Development and Validation Study.Journal of medical Internet research · 2023Article
- Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost.Frontiers in public health · 2023Article
- Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury.Journal of clinical medicine · 2022Article
- Machine learning for the prediction of acute kidney injury in patients after cardiac surgery.Frontiers in surgery · 2022Article
- Long-Term Consequences of Increased Activity of Urine Enzymes After Cardiac Surgery - A Prospective Observational Study.Therapeutics and clinical risk management · 2022Article
- Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering.Medical sciences (Basel, Switzerland) · 2021Article
- Subtyping Hyperchloremia among Hospitalized Patients by Machine Learning Consensus Clustering.Medicina (Kaunas, Lithuania) · 2021Article
Corrections and comments
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Authors and funding
7 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.
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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.