… We additionally demonstrate the importance of interpretability, which is supported by
attention mechanisms, while providing examples of explainable ADE predictions from the best …

… Adverse drug reactions (ADR) are estimated to be the fourth leading cause of death in the
United States – ahead of pulmonary disease (before the COVID-19 pandemic), diabetes, AIDS…

… Hence, the introduction and application of Explainable Artificial Intelligence (XAI) paradigm
has … Also, the chapter discusses the challenges facing using eXplainable AI in genomic …

… Consequently, the explainable data analytics helps people to get a better understanding of
… , detecting, controlling and combating the disease. In this paper, we present an explainable …

… Explainable AI (XAI) techniques have the potentiality to … Currently the explainability of models
applied to those data has … and remote settings, and explainability becomes fundamental to …

… of drugs and adverse drug reactions. It provides a unified framework to harvest and visualize
pathways that are associated with drugs … more transparent and explainable. Such function …

… (NLP) approaches in detecting adverse drug reactions from EHR and clinical reports [9]. …
baseline machine learning models, its lack of explainability made it difficult to use in our project …

… Explainable AI can also play a pivotal role in the design of clinical trials, by providing … to
anticipate adverse drug reactions (ADR) and aid correct assessments in view of drug approval …

… A recent study reports that 6.7% of hospitalized patients suffer from severe adverse drug
reactions in the United States [2]. Therefore, predicting drug-drug adverse reactions (DDADRs) …

… While deep neural networks were often referred to initially as black boxes, a lot of
research has gone into making deep networks explainable, as we show in Section 3. …