A critical issue in the usage of cancer drugs is its association with various adverse events (AEs) in some, but not all, patients. The National Cancer Institute (NCI) Common Terminology Criteria for Adverse Events (CTCAE) is a controlled terminology ...
Social networks, such as Twitter, have become important sources for active monitoring of user-reported adverse drug reactions (ADRs). Automatic extraction of ADR information can be crucial for healthcare providers, drug manufacturers, and consumers. ...
OBJECTIVE: A drug-drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prev...
Detecting drug-drug interactions (DDI) is important because information on DDIs can help prevent adverse effects from drug combinations. Since there are many new DDI-related papers published in the biomedical domain, manually extracting DDI informati...
Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from curr...
With the development of Web 2.0 technology, social media websites have become lucrative but under-explored data sources for extracting adverse drug events (ADEs), which is a serious health problem. Besides ADE, other semantic relation types (e.g., dr...
With increased usage of cardiovascular drugs (CVDs) for treating cardiovascular diseases, it is important to analyze CVD-associated adverse events (AEs). In this study, we systematically collected package insert-reported AEs associated with CVDs used...
Many drugs commonly prescribed during pregnancy lack a fetal safety recommendation - called FDA 'category C' drugs. This study aims to classify these drugs into harmful and safe categories using knowledge gained from chemoinformatics (i.e., pharmacol...
Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and int...
Adverse drug reactions (ADRs) are an essential part of the analysis of drug use, measuring drug use benefits, and making policy decisions. Traditional channels for identifying ADRs are reliable but very slow and only produce a small amount of data. T...
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