AIMC Topic: Adverse Drug Reaction Reporting Systems

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Advancing the State of the Art in Clinical Natural Language Processing through Shared Tasks.

Yearbook of medical informatics Aug 29, 2018
OBJECTIVES:  To review the latest scientific challenges organized in clinical Natural Language Processing (NLP) by highlighting the tasks, the most effective methodologies used, the data, and the sharing strategies.
Electronic Health Records Social Media Drug Labeling Natural Language Processing Confidentiality Adverse Drug Reaction Reporting Systems
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Mining heterogeneous networks with topological features constructed from patient-contributed content for pharmacovigilance.

Artificial intelligence in medicine Aug 6, 2018
Drug safety, also called pharmacovigilance, represents a serious health problem all over the world. Adverse drug reactions (ADRs) and drug-drug interactions (DDIs) are two important issues in pharmacovigilance, and how to detect drug safety signals h...
Risk Assessment Drug Interactions Humans Supervised Machine Learning Patient Safety Adverse Drug Reaction Reporting Systems Drug-Related Side Effects and Adverse Reactions Health Information Exchange Data Mining Databases, Factual Online Social Networking Pharmacovigilance Risk Factors
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Natural Language Processing and Its Implications for the Future of Medication Safety: A Narrative Review of Recent Advances and Challenges.

Pharmacotherapy Jul 22, 2018
The safety of medication use has been a priority in the United States since the late 1930s. Recently, it has gained prominence due to the increasing amount of data suggesting that a large amount of patient harm is preventable and can be mitigated wit...
Electronic Health Records Natural Language Processing Internet Humans Adverse Drug Reaction Reporting Systems Periodicals as Topic Drug-Related Side Effects and Adverse Reactions
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Evaluation of Natural Language Processing (NLP) systems to annotate drug product labeling with MedDRA terminology.

Journal of biomedical informatics Jun 1, 2018
INTRODUCTION: The FDA Adverse Event Reporting System (FAERS) is a primary data source for identifying unlabeled adverse events (AEs) in a drug or biologic drug product's postmarketing phase. Many AE reports must be reviewed by drug safety experts to ...
United States Food and Drug Administration Pharmacovigilance Terminology as Topic Natural Language Processing Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems Drug Labeling Humans United States
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SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media.

Artificial intelligence in medicine Oct 27, 2017
With the development of Web 2.0 technology, social media websites have become lucrative but under-explored data sources for extracting adverse drug events (ADEs), which is a serious health problem. Besides ADE, other semantic relation types (e.g., dr...
Supervised Machine Learning Drug-Related Side Effects and Adverse Reactions Data Mining Adverse Drug Reaction Reporting Systems Humans Databases, Factual Social Media
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Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records.

Journal of healthcare engineering Sep 26, 2017
Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and int...
Linear Models Pattern Recognition, Automated Adverse Drug Reaction Reporting Systems Electronic Health Records Language Humans Supervised Machine Learning Natural Language Processing Knowledge Bases Software Drug-Related Side Effects and Adverse Reactions Information Storage and Retrieval Algorithms Medical Informatics
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Application of Natural Language Processing and Network Analysis Techniques to Post-market Reports for the Evaluation of Dose-related Anti-Thymocyte Globulin Safety Patterns.

Applied clinical informatics Apr 26, 2017
OBJECTIVE: To evaluate the feasibility of automated dose and adverse event information retrieval in supporting the identification of safety patterns.
Antilymphocyte Serum Adverse Drug Reaction Reporting Systems Time Factors Dose-Response Relationship, Drug Natural Language Processing Feasibility Studies Humans Safety Data Mining
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Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports.

Artificial intelligence in medicine Feb 10, 2017
OBJECTIVE: Drug-drug interaction (DDI) is of serious concern, causing over 30% of all adverse drug reactions and resulting in significant morbidity and mortality. Early discovery of adverse DDI is critical to prevent patient harm. Spontaneous reporti...
Humans Machine Learning Bayes Theorem Data Mining Adverse Drug Reaction Reporting Systems Drug Interactions Drug-Related Side Effects and Adverse Reactions
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Classification-by-Analogy: Using Vector Representations of Implicit Relationships to Identify Plausibly Causal Drug/Side-effect Relationships.

AMIA ... Annual Symposium proceedings. AMIA Symposium Feb 10, 2017
An important aspect of post-marketing drug surveillance involves identifying potential side-effects utilizing adverse drug event (ADE) reporting systems and/or Electronic Health Records. These data are noisy, necessitating identified drug/ADE associa...
Support Vector Machine Electronic Health Records Humans Adverse Drug Reaction Reporting Systems Drug-Related Side Effects and Adverse Reactions Machine Learning Product Surveillance, Postmarketing ROC Curve Models, Theoretical
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Accuracy of an automated knowledge base for identifying drug adverse reactions.

Journal of biomedical informatics Dec 16, 2016
INTRODUCTION: Drug safety researchers seek to know the degree of certainty with which a particular drug is associated with an adverse drug reaction. There are different sources of information used in pharmacovigilance to identify, evaluate, and disse...
Pharmacovigilance Electronic Data Processing Adverse Drug Reaction Reporting Systems Drug-Related Side Effects and Adverse Reactions Knowledge Bases Humans
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