AIMC Topic: Adverse Drug Reaction Reporting Systems

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Comparing a Large Language Model with Previous Deep Learning Models on Named Entity Recognition of Adverse Drug Events.

Studies in health technology and informatics Aug 22, 2024
The ability to fine-tune pre-trained deep learning models to learn how to process a downstream task using a large training set allow to significantly improve performances of named entity recognition. Large language models are recent models based on t...
Natural Language Processing Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems Humans Deep Learning
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Navigating the Complexities of Artificial Intelligence-Enabled Real-World Data Collection for Oncology Pharmacovigilance.

JCO clinical cancer informatics May 1, 2024
This new editorial discusses the promise and challenges of successful integration of natural language processing methods into electronic health records for timely, robust, and fair oncology pharmacovigilance.
Data Collection Pharmacovigilance Medical Oncology Neoplasms Natural Language Processing Adverse Drug Reaction Reporting Systems Artificial Intelligence Humans Electronic Health Records
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Current Scenario and Future Prospects of Adverse Drug Reactions (ADRs) Monitoring and Reporting Mechanisms in the Rural Areas of India.

Current drug safety Jan 1, 2024
Pharmacovigilance (PV) deals with the detection, collection, assessment, understanding, and prevention of adverse effects associated with drugs. The objective of PV is to ensure the safety of the medicines and patients by monitoring and reporting all...
India Pharmacovigilance Adverse Drug Reaction Reporting Systems Health Knowledge, Attitudes, Practice Drug-Related Side Effects and Adverse Reactions Humans Artificial Intelligence
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Extracting postmarketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning.

Journal of the American Medical Informatics Association : JAMIA Jul 14, 2021
OBJECTIVE: Automated analysis of vaccine postmarketing surveillance narrative reports is important to understand the progression of rare but severe vaccine adverse events (AEs). This study implemented and evaluated state-of-the-art deep learning algo...
Humans Influenza Vaccines Adverse Drug Reaction Reporting Systems Guillain-Barre Syndrome Computer Systems United States Deep Learning
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Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text.

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2021
OBJECTIVE: We sought to demonstrate the feasibility of utilizing deep learning models to extract safety signals related to the use of dietary supplements (DSs) in clinical text.
Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems Natural Language Processing Feasibility Studies Deep Learning Dietary Supplements Humans
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[Potential for Big Data Analysis Using AI in the Field of Clinical Pharmacy].

Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan Jan 1, 2021
Industrial reforms utilizing artificial intelligence (AI) have advanced remarkably in recent years. The application of AI to big data analysis in the medical information field has also been advancing and is expected to be used to find drug adverse ef...
Adverse Drug Reaction Reporting Systems Pharmacy Service, Hospital Drug Repositioning Drug Interactions Drug-Related Side Effects and Adverse Reactions Data Analysis Big Data Humans Artificial Intelligence
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Follow-Up on the Use of Machine Learning in Clinical Quality Assurance: Can We Detect Adverse Event Under-Reporting in Oncology Trials?

Drug safety Mar 1, 2020
Humans Machine Learning Quality Indicators, Health Care Antineoplastic Agents Drug-Related Side Effects and Adverse Reactions Adverse Drug Reaction Reporting Systems Research Report Quality Assurance, Health Care Patient Safety Neoplasms Clinical Trials as Topic
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Application of Augmented Intelligence for Pharmacovigilance Case Seriousness Determination.

Drug safety Jan 1, 2020
INTRODUCTION: Identification of adverse events and determination of their seriousness ensures timely detection of potential patient safety concerns. Adverse event seriousness is a key factor in defining reporting timelines and is often performed manu...
Drug-Related Side Effects and Adverse Reactions Pharmacovigilance Adverse Drug Reaction Reporting Systems Humans Neural Networks, Computer
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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 Adverse Drug Reaction Reporting Systems Hospitals International Classification of Diseases Clinical Coding ROC Curve Algorithms Drug-Related Side Effects and Adverse Reactions Hospitalization
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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...
Adverse Drug Reaction Reporting Systems Drug-Related Side Effects and Adverse Reactions Pharmacovigilance Databases, Factual Machine Learning Humans Guideline Adherence Decision Making Algorithms Artificial Intelligence Workload Patient Safety Cognition
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