AIMC Topic: Drug-Related Side Effects and Adverse Reactions

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Network-Based Assessment of Adverse Drug Reaction Risk in Polypharmacy Using High-Throughput Screening Data.

International journal of molecular sciences Jan 17, 2019
The risk of adverse drug reactions increases in a polypharmacology setting. High-throughput drug screening with transcriptomics applied to human cells has shown that drugs have effects on several molecular pathways, and these affected pathways may be...
Neural Networks, Computer Algorithms Risk Assessment Humans Polypharmacy Drug-Related Side Effects and Adverse Reactions High-Throughput Screening Assays Drug Design Data Interpretation, Statistical
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A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records.

BMC medical informatics and decision making Jan 10, 2019
BACKGROUND: Adverse drug events (ADEs) as well as other preventable adverse events in the hospital setting incur a yearly monetary cost of approximately $3.5 billion, in the United States alone. Therefore, it is of paramount importance to reduce the ...
Humans Electronic Health Records Data Mining Hospitals Machine Learning Medical Informatics Applications Models, Statistical Drug-Related Side Effects and Adverse Reactions
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eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates.

BMC pharmacology & toxicology Jan 8, 2019
BACKGROUND: The efficiency of drug development defined as a number of successfully launched new pharmaceuticals normalized by financial investments has significantly declined. Nonetheless, recent advances in high-throughput experimental techniques an...
Humans Animals Machine Learning Drug Discovery Algorithms Drug-Related Side Effects and Adverse Reactions
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Predicting adverse drug reactions through interpretable deep learning framework.

BMC bioinformatics Dec 28, 2018
BACKGROUND: Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial cos...
Humans Deep Learning Drug-Related Side Effects and Adverse Reactions Models, Theoretical Back Pain Area Under Curve Necrosis
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Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies.

Journal of clinical pharmacy and therapeutics Dec 18, 2018
WHAT IS KNOWN AND OBJECTIVE: Drug-drug interactions (DDI) are frequent causes of adverse clinical drug reactions. Efforts have been directed at the early stage to achieve accurate identification of DDI for drug safety assessments, including the devel...
Humans Databases, Factual Computer Simulation Machine Learning Pharmacovigilance Reproducibility of Results Drug Interactions Support Vector Machine Drug-Related Side Effects and Adverse Reactions
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Innovation in Pharmacovigilance: Use of Artificial Intelligence in Adverse Event Case Processing.

Clinical pharmacology and therapeutics Dec 11, 2018
Automation of pharmaceutical safety case processing represents a significant opportunity to affect the strongest cost driver for a company's overall pharmacovigilance budget. A pilot was undertaken to test the feasibility of using artificial intellig...
Drug-Related Side Effects and Adverse Reactions Pharmacovigilance Artificial Intelligence Databases, Factual Adverse Drug Reaction Reporting Systems Humans
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An automated pipeline for analyzing medication event reports in clinical settings.

BMC medical informatics and decision making Dec 7, 2018
BACKGROUND: Medication events in clinical settings are significant threats to patient safety. Analyzing and learning from the medication event reports is an important way to prevent the recurrence of these events. Currently, the analysis of medicatio...
Drug-Related Side Effects and Adverse Reactions Support Vector Machine Risk Management Humans Machine Learning Neural Networks, Computer Patient Safety
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Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach.

Molecules (Basel, Switzerland) Dec 4, 2018
Inferring potential adverse drug reactions is an important and challenging task for the drug discovery and healthcare industry. Many previous studies in computational pharmacology have proposed utilizing multi-source drug information to predict drug ...
Machine Learning Algorithms Drug-Related Side Effects and Adverse Reactions Neural Networks, Computer Drug Discovery
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Tox_(R)CNN: Deep learning-based nuclei profiling tool for drug toxicity screening.

PLoS computational biology Nov 30, 2018
Toxicity is an important factor in failed drug development, and its efficient identification and prediction is a major challenge in drug discovery. We have explored the potential of microscopy images of fluorescently labeled nuclei for the prediction...
Neural Networks, Computer Algorithms Deep Learning Image Interpretation, Computer-Assisted Cell Nucleus Drug-Related Side Effects and Adverse Reactions Fluorescent Dyes Automation Indoles
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Identification of Drug-Side Effect Association via Semisupervised Model and Multiple Kernel Learning.

IEEE journal of biomedical and health informatics Nov 28, 2018
Drug-side effect association contains the information on marketed medicines and their recorded adverse drug reactions. Traditional experimental method is time consuming and expensive. All associations of drugs and side-effects are seen as a bipartite...
Supervised Machine Learning Medical Informatics Models, Statistical Humans Drug-Related Side Effects and Adverse Reactions Area Under Curve Algorithms
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