ArticleScientific reports2021
Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- Role of artificial intelligence in predicting the renal function after nephrectomy in renal cell carcinoma: a systematic review and meta-analysis.International urology and nephrology · 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
- Prediction of Acute Kidney Injury in Oncology: From Clinical Risk Scores to AI-Enabled Precision Onco-Nephrology.Cancers · 2026Review
- Identification of acute kidney injury after non-cardiac surgery by machine learning leveraging a large electronic health record database.Scientific reports · 2026Article
- Predicting distant metastasis in early-onset kidney cancer using machine learning: a SEER database study with external validation.Clinical and experimental medicine · 2025Article
- Transparent AI-driven personalized risk prediction system for acute kidney injury after total hip arthroplasty.Scientific reports · 2025Article
- Predicting postoperative fever in culture-negative patients undergoing mini-PCNL using MAP score-augmented machine learning: a retrospective cohort study.World journal of urology · 2025Article
- Prediction Model of Intradialytic Hypertension in Hemodialysis Patients Based on Machine Learning.Journal of medical systems · 2025Article
- Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges.Chronic diseases and translational medicine · 2025Review
- Personalized Prediction of Long-Term Renal Function Prognosis Following Nephrectomy Using Interpretable Machine Learning Algorithms: Case-Control Study.JMIR medical informatics · 2024Article
- Machine learning models to predict systemic inflammatory response syndrome after percutaneous nephrolithotomy.BMC urology · 2024Article
- Preoperative Age and Its Impact on Long-Term Renal Functional Decline after Robotic-Assisted Partial Nephrectomy: Insights from a Tertiary Referral Center.Medicina (Kaunas, Lithuania) · 2024Article
- The Present and Future of Artificial Intelligence in Urological Cancer.Journal of clinical medicine · 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
- Using machine learning to predict lymph node metastasis in patients with renal cell carcinoma: A population-based study.Frontiers in public health · 2023Article
- Machine learning for acute kidney injury: Changing the traditional disease prediction mode.Frontiers in medicine · 2023Review
- Artificial Intelligence and Machine Learning in Perioperative Acute Kidney Injury.Advances in kidney disease and health · 2023Review
- Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury.Journal of clinical medicine · 2022Article
- Development of artificial neural networks for early prediction of intestinal perforation in preterm infants.Scientific reports · 2022Article
- Comparison Between Statistical Model and Machine Learning Methods for Predicting the Risk of Renal Function Decline Using Routine Clinical Data in Health Screening.Risk management and healthcare policy · 2022Article
Corrections and comments
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Authors and funding
16 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. A total of 4104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. ML models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Postoperative AKI developed in 1167 patients (28.4%). All the ML models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783-0.837). The decision curve analysis demonstrated a greater net benefit of the ML models than the LR-based scoring model over all the ranges of threshold probabilities. The application of ML algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.
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