ArticleTherapeutic advances in drug safety2026
Machine learning studies of drug-induced nephrotoxicity: a scoping review.
Article in Therapeutic advances in drug safety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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4 authors.
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Abstract
Background: Machine learning methods have emerged as a promising approach to prevent drug-induced nephrotoxicity. Objective: This review evaluates the quality and highlights recent advances of machine learning algorithms for predicting drug-induced nephrotoxicity. Eligibility criteria: Studies on machine learning models to predict drug-induced acute kidney injury, acute kidney disease, or both published between January 2014 and August 2024 were eligible. Sources of evidence: A comprehensive search was conducted by using PubMed, Embase, Web of Science, Cochrane Library, and Scopus. Charting methods: A standardized charting form was developed based on CHARMS, TRIPOD+AI, and PROBAST tools to assess the quality and risk of bias across studies. Results: From the initial 5,179 articles searched, 24 studies were included in this review. All studies achieved good area under the receiver operating characteristic curves (AUROCs) above 0.75, with boosting machines being the most frequently outperforming algorithms ( Conclusion: Recent machine learning studies have demonstrated great performance using clinically obtainable features. Incorporating acute kidney injury and disease, methodological enhancement, and guideline adherence can facilitate clinical applicability in preventing drug-induced nephrotoxicity.
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