ArticlePeerJ2020
A comparative study of machine learning algorithms for predicting acute kidney injury after liver cancer resection.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation.Journal of medical Internet research · 2025Article
- Integrating Pre- and Postoperative Systemic Inflammatory Markers for Acute Kidney Injury Prediction Following Radical Cystectomy: A Multi-Center Retrospective Study.Journal of inflammation research · 2025Article
- Development and validation of a machine learning-based early prediction model for massive intraoperative bleeding in patients with primary hepatic malignancies.World journal of gastrointestinal oncology · 2024Article
- A deep learning approach for acute liver failure prediction with combined fully connected and convolutional neural networks.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Alternative Polyadenylation Regulatory Factors Signature for Survival Prediction in Kidney Renal Cell Carcinoma.Cancer informatics · 2024Article
- Artificial Intelligence and Machine Learning in Perioperative Acute Kidney Injury.Advances in kidney disease and health · 2023Review
- Machine learning for acute kidney injury: Changing the traditional disease prediction mode.Frontiers in medicine · 2023Review
- Machine Learning Model for Predicting Postoperative Survival of Patients with Colorectal Cancer.Cancer research and treatment · 2022Article
- Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms.Frontiers in public health · 2022Article
- Does Artificial Intelligence Make Clinical Decision Better? A Review of Artificial Intelligence and Machine Learning in Acute Kidney Injury Prediction.Healthcare (Basel, Switzerland) · 2021Review
- Skin autofluorescence as a novel predictor of acute kidney injury after liver resection.World journal of surgical oncology · 2021Observational
- Comparison of Prediction Models for Acute Kidney Injury Among Patients with Hepatobiliary Malignancies Based on XGBoost and LASSO-Logistic Algorithms.International journal of general medicine · 2021Article
- Employment of Artificial Intelligence Based on Routine Laboratory Results for the Early Diagnosis of Multiple Myeloma.Frontiers in oncology · 2021Article
- Machine Learning Can Predict Total Death After Radiofrequency Ablation in Liver Cancer Patients.Clinical Medicine Insights. Oncology · 2021Article
- Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches.Journal of clinical medicine · 2020Article
- Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation.Journal of clinical medicine · 2020Article
- Article
- Role of artificial intelligence in diagnosing pericoronal radiolucency.Journal of oral biology and craniofacial researchArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveMachine learning methods may have better or comparable predictive ability than traditional analysis. We explore machine learning methods to predict the likelihood of acute kidney injury after liver cancer resection.
methodsThis is a secondary analysis cohort study. We reviewed data from patients who had undergone resection of primary hepatocellular carcinoma between January 2008 and October 2015.
resultsThe analysis included 1,173 hepatectomy patients, 77 (6.6%) of whom had AKI and 1,096 (93.4%) who did not. The importance matrix for the Gbdt algorithm model shows that age, cholesterol, tumor size, surgery duration and PLT were the five most important parameters. Figure 1 shows that Age, tumor size and surgery duration had weak positive correlations with AKI. Cholesterol and PLT also had weak negative correlations with AKI. The models constructed by the four machine learning algorithms in the training group were compared. Among the four machine learning algorithms, random forest and gbm had the highest accuracy, 0.989 and 0.970 respectively. The precision of four of the five algorithms was 1, random forest being the exception. Among the test group, gbm had the highest accuracy (0.932). Random forest and gbm had the highest precision, both being 0.333. The AUC values for the four algorithms were: Gbdt (0.772), gbm (0.725), forest (0.662) and DecisionTree (0.628).
conclusionsMachine learning technology can predict acute kidney injury after hepatectomy. Age, cholesterol, tumor size, surgery duration and PLT influence the likelihood and development of postoperative acute kidney injury.
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