AIMC Topic: Drug-Related Side Effects and Adverse Reactions

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Utilizing Deep Learning for Detecting Adverse Drug Events in Structured and Unstructured Regulatory Drug Data Sets.

Pharmaceutical medicine Jul 24, 2022
BACKGROUND: The US Food and Drug Administration (FDA) collects and retains several data sets on post-market drugs and associated adverse events (AEs). The FDA Adverse Event Reporting System (FAERS) contains millions of AE reports submitted by the pub...
United States Food and Drug Administration Adverse Drug Reaction Reporting Systems Deep Learning United States Pharmaceutical Preparations Drug-Related Side Effects and Adverse Reactions Humans
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A novel machine learning model based on sparse structure learning with adaptive graph regularization for predicting drug side effects.

Journal of biomedical informatics Jul 14, 2022
Drug side effects are closely related to the success and failure of drug development. Here we present a novel machine learning method for side effect prediction. The proposed method treats side effect prediction as a multi-label learning problem and ...
Humans Machine Learning Drug Development Drug-Related Side Effects and Adverse Reactions Algorithms Research Design
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Multi-type feature fusion based on graph neural network for drug-drug interaction prediction.

BMC bioinformatics Jun 10, 2022
BACKGROUND: Drug-Drug interactions (DDIs) are a challenging problem in drug research. Drug combination therapy is an effective solution to treat diseases, but it can also cause serious side effects. Therefore, DDIs prediction is critical in pharmacol...
Neural Networks, Computer Drug Interactions Humans Gene Fusion Drug-Related Side Effects and Adverse Reactions
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idse-HE: Hybrid embedding graph neural network for drug side effects prediction.

Journal of biomedical informatics May 28, 2022
In drug development, unexpected side effects are the main reason for the failure of candidate drug trials. Discovering potential side effects of drugsin silicocan improve the success rate of drug screening. However, most previous works extracted and ...
Knowledge Drug-Related Side Effects and Adverse Reactions Humans Neural Networks, Computer Software Drug Development
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Integrative analysis of chemical properties and functions of drugs for adverse drug reaction prediction based on multi-label deep neural network.

Journal of integrative bioinformatics May 19, 2022
The prediction of adverse drug reactions (ADR) is an important step of drug discovery and design process. Different drug properties have been employed for ADR prediction but the prediction capability of drug properties and drug functions in integrate...
Neural Networks, Computer Drug-Related Side Effects and Adverse Reactions Humans
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Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations.

Drug safety May 17, 2022
The tools of artificial intelligence (AI) have enormous potential to enhance activities in pharmacovigilance. Pharmacovigilance experts need not be AI experts, but they should know enough about AI to explore the possibilities of collaboration with th...
Drug-Related Side Effects and Adverse Reactions Data Mining Machine Learning Algorithms Pharmacovigilance Humans Artificial Intelligence
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Identification of hand-foot syndrome from cancer patients' blog posts: BERT-based deep-learning approach to detect potential adverse drug reaction symptoms.

PloS one May 4, 2022
Early detection and management of adverse drug reactions (ADRs) is crucial for improving patients' quality of life. Hand-foot syndrome (HFS) is one of the most problematic ADRs for cancer patients. Recently, an increasing number of patients post thei...
Deep Learning Drug-Related Side Effects and Adverse Reactions Natural Language Processing Neoplasms Humans Hand-Foot Syndrome Quality of Life
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In silico prediction of potential drug-induced nephrotoxicity with machine learning methods.

Journal of applied toxicology : JAT Apr 29, 2022
In recent years, drug-induced nephrotoxicity has been one of the main reasons for the failure of drug development. Early prediction of the nephrotoxicity for drug candidates is critical to the success of clinical trials. Therefore, it is very importa...
Drug Discovery Support Vector Machine Drug-Related Side Effects and Adverse Reactions Algorithms Computer Simulation Machine Learning Humans
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Evaluation Analysis of the Nephrotoxicity of Preparations with CONSORT Harms Statement Based on Deep Learning.

Journal of healthcare engineering Apr 7, 2022
In this paper, the safety of polyglycoside (TW) preparation was evaluated by combining literature research and evidence-based evaluation research, so as to provide evidence-based safety information of polyglycoside preparation (nephroptosis) for go...
Humans Drugs, Chinese Herbal Tripterygium Deep Learning Drug-Related Side Effects and Adverse Reactions Medicine, Chinese Traditional
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Machine learning approach to identify adverse events in scientific biomedical literature.

Clinical and translational science Apr 3, 2022
Monitoring the occurrence of adverse events in the scientific literature is a mandatory process in drug marketing surveillance. This is a very time-consuming and complex task to fulfill the compliance and, most importantly, to ensure patient safety. ...
Humans Publications Machine Learning Drug-Related Side Effects and Adverse Reactions Patient Safety
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