Motivation Adverse events resulting from drug-drug interactions (DDI) pose a serious health
issue. The ability to automatically extract DDIs described in the biomedical literature could …

Background Drug-drug interaction extraction (DDI) needs assistance from automated
methods to address the explosively increasing biomedical texts. In recent years, deep neural …

Drug‐drug interaction (DDI) extraction as a typical relation extraction task in natural
language processing (NLP) has always attracted great attention. Most state‐of‐the‐art DDI …

Motivation Neural methods to extract drug–drug interactions (DDIs) from literature require a
large number of annotations. In this study, we propose a novel method to effectively utilize …

Abstract Background Drug-drug interactions (DDIs) often bring unexpected side effects. The
clinical recognition of DDIs is a crucial issue for both patient safety and healthcare cost …

We propose a novel attention mechanism for a Convolutional Neural Network (CNN)-based
Drug-Drug Interaction (DDI) extraction model. CNNs have been shown to have a great …

Extracting drug-drug interaction (DDI) relations is one of the most typical tasks in the field of
biomedical relation extraction. Automatic DDI extraction from the biomedical corpus is …

We propose a novel neural method to extract drug-drug interactions (DDIs) from texts using
external drug molecular structure information. We encode textual drug pairs with …

Drug-drug interaction (DDI) is a vital information when physicians and pharmacists intend to
co-administer two or more drugs. Thus, several DDI databases are constructed to avoid …

Motivation: Knowledge of drug–drug interactions (DDIs) is crucial for health-care
professionals to avoid adverse effects when co-administering drugs to patients. As most …