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Machine learning-based methods and novel data models to predict adverse drug reaction

Predicting adverse drug reactions (ADRs) plays a critical role in developing new drugs and preventing adverse reactions during the treatment of existing drugs. However, with the rapid progress of machine learning technology, a new situation has been opened up in ADRs prediction. Using appropriate machine learning methods with existing data can achieve high prediction performance, attracting more researchers. This review describes commonly used features (biological, chemical, and phenotypic features) and machine learning algorithms.