Supervised machine learning-based prediction for dry mouth oral adverse drug reactions

Adverse drug reactions (ADRs) are defined as an unintended and harmful response that occurs with the ingestion of a certain drug. ADRs result in an appreciably harmful or unpleasant reaction that determines the success or failure of a drug. In this way, the generation of effective models for the prediction of ADR during the drug development process is of high relevance for human health. In this work, we present a complete proposal based on...