AIMC Topic: Drug-Related Side Effects and Adverse Reactions

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Semi-Supervised Learning Algorithm for Identifying High-Priority Drug-Drug Interactions Through Adverse Event Reports.

IEEE journal of biomedical and health informatics Aug 2, 2019
Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. Generating proper DDI alerts during prescribing workflow has the potential to prevent DDI-related adverse events. However, ...
Humans Decision Support Systems, Clinical Algorithms Supervised Machine Learning Databases, Factual Adverse Drug Reaction Reporting Systems Drug-Related Side Effects and Adverse Reactions
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Artificial Intelligence for Drug Toxicity and Safety.

Trends in pharmacological sciences Aug 2, 2019
Interventional pharmacology is one of medicine's most potent weapons against disease. These drugs, however, can result in damaging side effects and must be closely monitored. Pharmacovigilance is the field of science that monitors, detects, and preve...
Animals Toxicity Tests Humans Drug Evaluation, Preclinical Artificial Intelligence Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems Machine Learning Pharmacovigilance Product Surveillance, Postmarketing Quantitative Structure-Activity Relationship
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Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit.

Molecules (Basel, Switzerland) Jul 25, 2019
Predicting novel uses for drugs using their chemical, pharmacological, and indication information contributes to minimizing costs and development periods. Most previous prediction methods focused on integrating the similarity and association informat...
ROC Curve Models, Theoretical Drug-Related Side Effects and Adverse Reactions Reproducibility of Results Deep Learning Neural Networks, Computer Algorithms Humans
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Improved Prediction of Drug-Target Interactions Using Self-Paced Learning with Collaborative Matrix Factorization.

Journal of chemical information and modeling Jul 11, 2019
Identifying drug-target interactions (DTIs) plays an important role in the field of drug discovery, drug side-effects, and drug repositioning. However, in vivo or biochemical experimental methods for identifying new DTIs are extremely expensive and t...
Machine Learning Drug Discovery Computational Biology Drug Repositioning Drug-Related Side Effects and Adverse Reactions
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In Silico Prediction of Hemolytic Toxicity on the Human Erythrocytes for Small Molecules by Machine-Learning and Genetic Algorithm.

Journal of medicinal chemistry Jul 8, 2019
Hemolytic toxicity of small molecules, as one of the important ADMET end points, can cause the lysis of erythrocytes membrane and leaking of hemoglobin into the blood plasma, which leads to various side effects. Thus, it is very crucial to assess the...
Computer Simulation Drug-Related Side Effects and Adverse Reactions Erythrocytes Software Algorithms Hemolysis Small Molecule Libraries Humans Machine Learning
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Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer.

International journal of medical informatics May 25, 2019
BACKGROUND: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through all discharge summaries to ...
Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems Medical Errors Singapore Humans Patient Discharge Machine Learning Electronic Health Records Algorithms Natural Language Processing
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Knowledge-guided convolutional networks for chemical-disease relation extraction.

BMC bioinformatics May 21, 2019
BACKGROUND: Automatic extraction of chemical-disease relations (CDR) from unstructured text is of essential importance for disease treatment and drug development. Meanwhile, biomedical experts have built many highly-structured knowledge bases (KBs), ...
Drug-Related Side Effects and Adverse Reactions Knowledge Bases Disease Humans Data Mining
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Smoothing dense spaces for improved relation extraction between drugs and adverse reactions.

International journal of medical informatics May 13, 2019
BACKGROUND AND OBJECTIVE: This work aims at extracting Adverse Drug Reactions (ADRs), i.e. a harm directly caused by a drug at normal doses, from Electronic Health Records (EHRs). The lack of readily available EHRs because of confidentiality issues a...
Humans Semantics Electronic Health Records Natural Language Processing Adverse Drug Reaction Reporting Systems Drug-Related Side Effects and Adverse Reactions
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An adverse drug effect mentions extraction method based on weighted online recurrent extreme learning machine.

Computer methods and programs in biomedicine Apr 30, 2019
BACKGROUND AND OBJECTIVE: Automatic extraction of adverse drug effect (ADE) mentions from biomedical texts is a challenging research problem that has attracted significant attention from the pharmacovigilance and biomedical text mining communities. I...
Publications Humans Drug-Related Side Effects and Adverse Reactions Software Pharmacovigilance Data Mining Databases, Factual Neural Networks, Computer Algorithms Semantics Machine Learning Reproducibility of Results
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Machine learning on adverse drug reactions for pharmacovigilance.

Drug discovery today Mar 12, 2019
Machine learning, especially deep learning, has the predictive power to predict adverse drug reactions, repurpose drugs and perform precision medicine. We provide a background of machine learning and propose a potential high-performance deep learning...
Drug-Related Side Effects and Adverse Reactions Humans Pharmacovigilance Machine Learning
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