Scope, design, and reporting of prediction models for antineoplastic drugs‐related adverse drug events: A systematic review of machine learning and traditional modeling.

This systematic review examines prediction models for adverse drug events (ADEs) related to antineoplastic drugs, which are used in cancer therapy. The review analyzes 55 studies and evaluates the quality and reporting of these prediction models. The majority of models focused on ADEs related to conventional chemotherapy drugs, with fewer models addressing novel anticancer drugs and additional toxicities. The study finds that machine learning algorithms generally had better methodological quality but weaker reporting quality compared to traditional statistical models. The authors recommend expanding research focus, optimizing model presentation, and improving the design, conduct, validation, and reporting of prediction models to enhance their application in clinical practice. [Extracted from the article]