AIMC Topic: Adverse Drug Reaction Reporting Systems

Clear Filters Showing 71 to 78 of 78 articles

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...
Computational Biology Models, Statistical Machine Learning Computer Simulation Adverse Drug Reaction Reporting Systems Databases, Pharmaceutical Drug-Related Side Effects and Adverse Reactions Knowledge Bases Humans
View on PubMed DOI

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...
Neural Networks, Computer Deep Learning Humans Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems
View on PubMed DOI

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.
Natural Language Processing Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems Humans Electronic Health Records
View on PubMed DOI

A chronological pharmacovigilance network analytics approach for predicting adverse drug events.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2018
OBJECTIVES: This study extends prior research by combining a chronological pharmacovigilance network approach with machine-learning (ML) techniques to predict adverse drug events (ADEs) based on the drugs' similarities in terms of the proteins they t...
Datasets as Topic Adverse Drug Reaction Reporting Systems Drug-Related Side Effects and Adverse Reactions Pharmacovigilance Computer Communication Networks Machine Learning Algorithms Models, Theoretical Drug Interactions Humans
View on PubMed DOI

Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review.

Drug safety Nov 1, 2017
The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug events (ADEs) with pharmaceutical products. This article is a comprehensive structured review of recent advances in applying natural language processing (NLP) ...
Adverse Drug Reaction Reporting Systems Drug-Related Side Effects and Adverse Reactions Pharmacovigilance Humans Electronic Health Records Natural Language Processing
View on PubMed DOI

Development of an automated assessment tool for MedWatch reports in the FDA adverse event reporting system.

Journal of the American Medical Informatics Association : JAMIA Sep 1, 2017
OBJECTIVE: As the US Food and Drug Administration (FDA) receives over a million adverse event reports associated with medication use every year, a system is needed to aid FDA safety evaluators in identifying reports most likely to demonstrate causal ...
Logistic Models United States Machine Learning Adverse Drug Reaction Reporting Systems Models, Theoretical ROC Curve Data Mining Natural Language Processing Support Vector Machine United States Food and Drug Administration Drug-Related Side Effects and Adverse Reactions
View on PubMed DOI

Adverse Drug Event Monitoring with Clinical and Laboratory Data Using Arden Syntax.

Studies in health technology and informatics Jan 1, 2017
In times of steadily increasing numbers of administered drugs, the detection of adverse drug events (ADEs) is an important aspect of improving patient safety. At present only about 1-13% of detected ADEs are reported. Raising the number of reported A...
Adverse Drug Reaction Reporting Systems Hyperkalemia Humans Knowledge Bases Drug Monitoring Patient Safety Pharmacovigilance Drug-Related Side Effects and Adverse Reactions
View on PubMed

Exploring brand-name drug mentions on Twitter for pharmacovigilance.

Studies in health technology and informatics Jan 1, 2015
Twitter has been proposed by several studies as a means to track public health trends such as influenza and Ebola outbreaks by analyzing user messages in order to measure different population features and interests. In this work we analyze the number...
Adverse Drug Reaction Reporting Systems Prescription Drugs Pharmacovigilance Natural Language Processing Terminology as Topic Population Surveillance Vocabulary, Controlled Data Mining Pattern Recognition, Automated Social Media
View on PubMed
Popular Topics
Recent Journals