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Drug-Related Side Effects and Adverse Reactions

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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 Computational Biology Humans Machine Learning Knowledge Bases Adverse Drug Reaction Reporting Systems Databases, Pharmaceutical Models, Statistical Drug-Related Side Effects and Adverse Reactions
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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 Drug-Related Side Effects and Adverse Reactions Neural Networks, Computer 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.
Adverse Drug Reaction Reporting Systems Natural Language Processing Drug-Related Side Effects and Adverse Reactions Humans Electronic Health Records
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Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding.

Drug safety Jan 1, 2019
INTRODUCTION: Adverse drug event (ADE) detection is a vital step towards effective pharmacovigilance and prevention of future incidents caused by potentially harmful ADEs. The electronic health records (EHRs) of patients in hospitals contain valuable...
Drug-Related Side Effects and Adverse Reactions Neural Networks, Computer Machine Learning Electronic Health Records Deep Learning Humans
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Overview of the First Natural Language Processing Challenge for Extracting Medication, Indication, and Adverse Drug Events from Electronic Health Record Notes (MADE 1.0).

Drug safety Jan 1, 2019
INTRODUCTION: This work describes the Medication and Adverse Drug Events from Electronic Health Records (MADE 1.0) corpus and provides an overview of the MADE 1.0 2018 challenge for extracting medication, indication, and adverse drug events (ADEs) fr...
Electronic Health Records Natural Language Processing Humans Medication Systems Drug-Related Side Effects and Adverse Reactions Pattern Recognition, Automated
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MADEx: A System for Detecting Medications, Adverse Drug Events, and Their Relations from Clinical Notes.

Drug safety Jan 1, 2019
INTRODUCTION: Early detection of adverse drug events (ADEs) from electronic health records is an important, challenging task to support pharmacovigilance and drug safety surveillance. A well-known challenge to use clinical text for detection of ADEs ...
Humans Machine Learning Drug-Related Side Effects and Adverse Reactions Electronic Health Records Natural Language Processing
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The Application of Machine Learning Techniques in Clinical Drug Therapy.

Current computer-aided drug design Jan 1, 2019
INTRODUCTION: The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adv...
Machine Learning Humans Drug-Related Side Effects and Adverse Reactions Drug Discovery Decision Trees Drug Therapy
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Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2018
OBJECTIVE: We executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related ...
Natural Language Processing Data Mining Support Vector Machine Drug-Related Side Effects and Adverse Reactions Pharmacovigilance Social Media Humans Neural Networks, Computer
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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...
Computer Communication Networks Algorithms Models, Theoretical Humans Machine Learning Drug Interactions Pharmacovigilance Datasets as Topic Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems
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Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2018
OBJECTIVE: The aim of this work is to leverage relational information extracted from biomedical literature using a novel synthesis of unsupervised pretraining, representational composition, and supervised machine learning for drug safety monitoring.
Drug-Related Side Effects and Adverse Reactions Data Mining Pharmacovigilance Semantics Natural Language Processing Supervised Machine Learning Humans Product Surveillance, Postmarketing Databases, Bibliographic
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