AIMC Topic: Drug-Related Side Effects and Adverse Reactions
Clear Filters Showing 121 to 130 of 322 articles
Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events.
International journal of environmental research and public health
Mar 5, 2021
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
Feb 23, 2021
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
Jan 25, 2021
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
Jan 25, 2021
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
Jan 20, 2021
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
Jan 12, 2021
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 ...
Assessing Drug Development Risk Using Big Data and Machine Learning.
Cancer research
Dec 22, 2020
Identifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, characterizing drug development risk, the probability that a drug will eventually receive regulatory approval, has been notoriously hard ...
Adverse Drug Reaction extraction: Tolerance to entity recognition errors and sub-domain variants.
Computer methods and programs in biomedicine
Dec 5, 2020
Predicting adverse drug reactions of two-drug combinations using structural and transcriptomic drug representations to train an artificial neural network.
Chemical biology & drug design
Oct 16, 2020
Adverse drug reactions (ADRs) are pharmacological events triggered by drug interactions with various sources of origin including drug-drug interactions. While there are many computational studies that explore models to predict ADRs originating from s...
Exploiting complex medical data with interpretable deep learning for adverse drug event prediction.
Artificial intelligence in medicine
Sep 15, 2020
A variety of deep learning architectures have been developed for the goal of predictive modelling and knowledge extraction from medical records. Several models have placed strong emphasis on temporal attention mechanisms and decay factors as a means ...
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