ArticlePloS one
Prediction model of acute kidney injury induced by cisplatin in older adults using a machine learning algorithm.
Article in PloS one. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis.Frontiers in pharmacology · 2024Pooled it
- Prediction of Acute Kidney Injury in Oncology: From Clinical Risk Scores to AI-Enabled Precision Onco-Nephrology.Cancers · 2026Review
- Machine learning studies of drug-induced nephrotoxicity: a scoping review.Therapeutic advances in drug safety · 2026Article
- Predicting Chronic Kidney Disease After Cisplatin Treatment Using Population-Level Data.JAMA oncology · 2025Article
- Prediction of Cisplatin-Induced Acute Kidney Injury Using an Interpretable Machine Learning Model and Electronic Medical Record Information.Clinical and translational science · 2025Article
- Development and validation of a LASSO prediction model for cisplatin induced nephrotoxicity: a case-control study in China.BMC nephrology · 2024Article
- Prediction Model for Severe Thrombocytopenia Induced by Gemcitabine Plus Cisplatin Combination Therapy in Patients with Urothelial Cancer.Clinical drug investigation · 2024Article
- Hypoalbuminemia and cisplatin-induced acute kidney injury.Frontiers in pharmacology · 2024Article
- Bibliometric and visual analysis of machine learning-based research in acute kidney injury worldwide.Frontiers in public health · 2023Article
- Machine learning for acute kidney injury: Changing the traditional disease prediction mode.Frontiers in medicine · 2023Review
- Machine learning versus binomial logistic regression analysis for fall risk based on SPPB scores in older adult outpatients.Digital healthArticle
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly detection and prediction of cisplatin-induced acute kidney injury (Cis-AKI) are essential for the management of patients on chemotherapy with cisplatin. This study aimed to evaluate the performance of a prediction model for Cis-AKI.
methodsJapanese patients, who received cisplatin as the first-line chemotherapy at Fujita Health University Hospital, were enrolled in the study. The main metrics for evaluating the machine learning model were the area under the curve (AUC), accuracy, precision, recall, and F-measure. In addition, the rank of contribution as a predictive factor of Cis-AKI was determined by machine learning.
resultsA total of 1,014 and 226 patients were assigned to the development and validation data groups, respectively. The current prediction model showed the highest performance in patients 65 years old and above (AUC: 0.78, accuracy: 0.77, precision: 0.38, recall: 0.70, F-measure: 0.49). The maximum daily cisplatin dose and serum albumin levels contributed the most to the prediction of Cis-AKI.
conclusionOur prediction model for Cis-AKI performed effectively in older patients.
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