AIMC Topic: Drug-Related Side Effects and Adverse Reactions

Clear Filters Showing 131 to 140 of 343 articles

Mining Toxicity Information from Large Amounts of Toxicity Data.

Journal of medicinal chemistry
Safety is a main reason for drug failures, and therefore, the detection of compound toxicity and potential adverse effects in the early stage of drug development is highly desirable. However, accurate prediction of many toxicity endpoints is extremel...

Application of Supervised SOM Algorithms in Predicting the Hepatotoxic Potential of Drugs.

International journal of molecular sciences
The hepatotoxic potential of drugs is one of the main reasons why a number of drugs never reach the market or have to be withdrawn from the market. Therefore, the evaluation of the hepatotoxic potential of drugs is an important part of the drug devel...

An Analytical Review of Computational Drug Repurposing.

IEEE/ACM transactions on computational biology and bioinformatics
Drug repurposing is a vital function in pharmaceutical fields and has gained popularity in recent years in both the pharmaceutical industry and research community. It refers to the process of discovering new uses and indications for existing or faile...

Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2.

Viruses
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes COVID-19 and is responsible for the ongoing pandemic. Screening of potential antiviral drugs against SARS-CoV-2 depend on in vitro experiments, which are based on the quantification ...

Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events.

International journal of environmental research and public health
While the clinical approval process is able to filter out medications whose utility does not offset their adverse drug reaction profile in humans, it is not well suited to characterizing lower frequency issues and idiosyncratic multi-drug interaction...

Root causes of adverse drug events in hospitals and artificial intelligence capabilities for prevention.

Journal of advanced nursing
AIMS: To identify and prioritize the root causes of adverse drug events (ADEs) in hospitals and to assess the ability of artificial intelligence (AI) capabilities to prevent ADEs.

Neural Multi-Task Learning for Adverse Drug Reaction Extraction.

AMIA ... Annual Symposium proceedings. AMIA Symposium
A reliable and searchable knowledge database of adverse drug reactions (ADRs) is highly important and valuable for improving patient safety at the point of care. In this paper, we proposed a neural multi-task learning system, NeuroADR, to extract ADR...

Leveraging digital media data for pharmacovigilance.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The development of novel drugs in response to changing clinical requirements is a complex and costly method with uncertain outcomes. Postmarket pharmacovigilance is essential as drugs often have under-reported side effects. This study intends to use ...

Prediction of adverse drug reactions using drug convolutional neural networks.

Journal of bioinformatics and computational biology
Prediction of Adverse Drug Reactions (ADRs) has been an important aspect of Pharmacovigilance because of its impact in the pharma industry. The standard process of introduction of a new drug into a market involves a lot of clinical trials and tests. ...

An ensemble learning approach for modeling the systems biology of drug-induced injury.

Biology direct
BACKGROUND: Drug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite being ...