Predicting adverse drug reactions in oncology: A critical review of machine learning approaches and future directions

Abstract

Because of polypharmacy and complicated treatment protocols, adverse drug reactions (ADRs) continue to be a major problem in oncology and frequently lead to serious clinical complications. Recent developments in the use of artificial intelligence (AI) and machine learning (ML) for ADR prediction in anticancer therapy are critically assessed in this review. We go over a variety of methods for utilizing both structured and unstructured clinical data, such as supervised, unsupervised, and deep learning models in addition to natural language processing (NLP) strategies. Strong performance has been demonstrated by ensemble techniques like Random Forest and Gradient Boosting, while deep neural networks allow for sophisticated feature extraction, albeit with interpretability issues. We highlight new integrative techniques based on current literature trends, such as integrating demographic information, treatment history, and physiological signals with CNN-based models and SHAP-based.