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Drug-drug interaction (DDI), as a possible result of drug combination treatment, could lead to adverse physiological reactions and increasing mortality rates of patients. Therefore, predicting potential DDI has always been an important and challenging ...

The field of Drug-drug interaction (DDI) aims to detect descriptions of interactions between drugs from biomedical texts. Currently, researchers have extracted DDIs using pre-trained language models such as BERT, which often misclassify two kinds of DDI ...

Language Models (LM) capture the characteristics of the distribution of words sequences in natural language, learning meaningful distributed representations in the process. Recent advancements in Neural Networks and Deep Leaning have led to rapid ...

Preventable adverse events as a result of medical errors present a growing concern in the healthcare system. As drug-drug interactions (DDIs) may lead to preventable adverse events, being able to extract DDIs from drug labels into a machine-processable ...

Interference between pharmacological substances can cause serious medical injuries. Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases but can also result in a reduction of drug development cost. Presently, most ...

Integrated approaches for pharmacology are required for the mechanism-based predictions of adverse drug reactions that manifest due to concomitant intake of multiple drugs. These approaches require the integration and analysis of biomedical data and ...

Drug-Drug Interactions (DDIs) are a major cause of preventable adverse drug reactions and a huge burden on public health and the healthcare system. On the other hand, there is a large amount of drug-related (open) data published on the Web, describing ...

Semantic web technologies can support the rapid and transparent validation of scientific claims by interconnecting the assumptions and evidence used to support or challenge assertions. One important application domain is medication safety, where more ...

Drug-drug interactions (DDIs) are a serious drug safety problem for health consumers and how to detect such interactions effectively and efficiently has been of great medical significance. Currently, methods proposed to detect DDIs are mainly based on ...

Adverse drug reactions (ADRs) are causing a substantial amount of hospital admissions and deaths, which cannot be underestimated. Drug-drug interactions (DDIs) are an important patient safety problem and have been reported to cause a large portion of ...

A drug-drug interaction (DDIs) occurs when one drug influences the level or activity of another drug. The detection of DDIs is an important research area in patient safety since these interactions can become very dangerous and increase health care ...

Multiple studies indicate that drug-drug interactions are a significant source of preventable adverse drug events. Factors contributing to the occurrence of preventable ADEs resulting from DDIs include a lack of knowledge of the patient's concurrent ...

DrugNerAR, a drug anaphora resolution system is presented to address the problem of co-referring expressions in pharmacological literature. This development is part of a larger and innovative study about automatic drug-drug interaction extraction. ...

Drug-drug interactions are frequently reported in biomedical literature and Information Extraction (IE) techniques have been devised as a useful instrument for managing this knowledge. Nevertheless, IE at the sentence level has a limited effect because ...