AIMC Topic: Drug-Related Side Effects and Adverse Reactions

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Drug knowledge bases and their applications in biomedical informatics research.

Briefings in bioinformatics Jul 19, 2019
Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives have been taken to help researchers make good use of them. As the results of these initiatives, many...
Social Media Drug Repositioning Drug Interactions Computational Biology Drug Development Systems Integration Databases, Pharmaceutical Pharmacogenomic Testing Drug-Related Side Effects and Adverse Reactions Knowledge Bases Data Mining Humans Machine Learning Algorithms
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An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models.

Journal of the American Medical Informatics Association : JAMIA Jul 1, 2019
OBJECTIVE: We aim to evaluate the effectiveness of advanced deep learning models (eg, capsule network [CapNet], adversarial training [ADV]) for single-domain and multidomain relation extraction from electronic health record (EHR) notes.
Natural Language Processing Drug-Related Side Effects and Adverse Reactions Data Mining Humans Electronic Health Records Deep Learning
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Comment on: "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts".

Journal of the American Medical Informatics Association : JAMIA Jun 1, 2019
Pharmacovigilance Datasets as Topic Natural Language Processing Drug-Related Side Effects and Adverse Reactions Data Mining Deep Learning Social Media Humans Neural Networks, Computer
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Reply to comment on: "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts".

Journal of the American Medical Informatics Association : JAMIA Jun 1, 2019
We appreciate the detailed review provided by Magge et al1 of our article, "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts." 2 In their letter, they present a subjectiv...
Pharmacovigilance Social Media Drug-Related Side Effects and Adverse Reactions Humans Neural Networks, Computer Deep Learning
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A Machine-Learning Algorithm to Optimise Automated Adverse Drug Reaction Detection from Clinical Coding.

Drug safety Jun 1, 2019
INTRODUCTION: Adverse drug reaction (ADR) detection in hospitals is heavily reliant on spontaneous reporting by clinical staff, with studies in the literature pointing to high rates of underreporting [1]. International Classification of Diseases, 10t...
Humans Machine Learning ROC Curve Drug-Related Side Effects and Adverse Reactions International Classification of Diseases Algorithms Hospitalization Hospitals Clinical Coding Adverse Drug Reaction Reporting Systems
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Artificial Intelligence Within Pharmacovigilance: A Means to Identify Cognitive Services and the Framework for Their Validation.

Pharmaceutical medicine Apr 1, 2019
INTRODUCTION: Pharmacovigilance (PV) detects, assesses, and prevents adverse events (AEs) and other drug-related problems by collecting, evaluating, and acting upon AEs. The volume of individual case safety reports (ICSRs) increases yearly, but it is...
Cognition Decision Making Humans Drug-Related Side Effects and Adverse Reactions Patient Safety Workload Machine Learning Artificial Intelligence Pharmacovigilance Databases, Factual Adverse Drug Reaction Reporting Systems Algorithms Guideline Adherence
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Artificial Intelligence and the Future of the Drug Safety Professional.

Drug safety Apr 1, 2019
The healthcare industry, and specifically the pharmacovigilance industry, recognizes the need to support the increasing amount of data received from individual case safety reports (ICSRs). To cope with this increase, more healthcare and qualified pro...
Decision Making Risk Management Drug-Related Side Effects and Adverse Reactions Pharmacovigilance Humans Machine Learning Artificial Intelligence Algorithms Health Personnel
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Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models.

Briefings in bioinformatics Jan 18, 2019
Timely identification of adverse drug reactions (ADRs) is highly important in the domains of public health and pharmacology. Early discovery of potential ADRs can limit their effect on patient lives and also make drug development pipelines more robus...
Computer Simulation Humans Databases, Pharmaceutical Drug-Related Side Effects and Adverse Reactions Machine Learning Computational Biology Knowledge Bases Models, Statistical Adverse Drug Reaction Reporting Systems
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Adverse Drug Events Detection in Clinical Notes by Jointly Modeling Entities and Relations Using Neural Networks.

Drug safety Jan 1, 2019
BACKGROUND AND SIGNIFICANCE: Adverse drug events (ADEs) occur in approximately 2-5% of hospitalized patients, often resulting in poor outcomes or even death. Extraction of ADEs from clinical narratives can accelerate and automate pharmacovigilance. U...
Deep Learning Humans Neural Networks, Computer Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems
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Detecting Adverse Drug Events with Rapidly Trained Classification Models.

Drug safety Jan 1, 2019
INTRODUCTION: Identifying occurrences of medication side effects and adverse drug events (ADEs) is an important and challenging task because they are frequently only mentioned in clinical narrative and are not formally reported.
Humans Electronic Health Records Natural Language Processing Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems
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