AIMC Topic: Pharmacovigilance

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Causal Deep Learning for the Detection of Adverse Drug Reactions: Drug-Induced Acute Kidney Injury as a Case Study.

Studies in health technology and informatics Aug 22, 2024
Causal Deep/Machine Learning (CDL/CML) is an emerging Artificial Intelligence (AI) paradigm. The combination of causal inference and AI could mine explainable causal relationships between data features, providing useful insights for various applicati...
Humans Acute Kidney Injury Drug-Related Side Effects and Adverse Reactions Pharmacovigilance Adverse Drug Reaction Reporting Systems Electronic Health Records 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.
Electronic Health Records Neoplasms Data Collection Medical Oncology Pharmacovigilance Adverse Drug Reaction Reporting Systems Natural Language Processing Humans Artificial Intelligence
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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 Health Knowledge, Attitudes, Practice Drug-Related Side Effects and Adverse Reactions Humans Artificial Intelligence Pharmacovigilance Adverse Drug Reaction Reporting Systems
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Overcoming Major Barriers to Build Efficient Decision Support Systems in Pharmacovigilance.

Studies in health technology and informatics Jun 29, 2022
Many decision support methods and systems in pharmacovigilance are built without explicitly addressing specific challenges that jeopardize their eventual success. We describe two sets of challenges and appropriate strategies to address them. The firs...
Decision Support Systems, Management Pharmacovigilance Natural Language Processing Data Accuracy User-Computer Interface Decision Support Systems, Clinical
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Named Entity Recognition in Pubmed Abstracts for Pharmacovigilance Using Deep Learning.

Studies in health technology and informatics May 25, 2022
Methods of natural language processing associated with machine learning or deep learning can support detection of adverse drug reactions in abstracts of case reports available on Pubmed. In 2012, Gurulingappa et al. proposed a training set for the re...
Natural Language Processing PubMed Pharmacovigilance Machine Learning Deep Learning
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DeepADEMiner: a deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter.

Journal of the American Medical Informatics Association : JAMIA Sep 18, 2021
OBJECTIVE: Research on pharmacovigilance from social media data has focused on mining adverse drug events (ADEs) using annotated datasets, with publications generally focusing on 1 of 3 tasks: ADE classification, named entity recognition for identify...
Drug-Related Side Effects and Adverse Reactions Pharmacovigilance Deep Learning Social Media Humans
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Semiautomated Approach for Muscle Weakness Detection in Clinical Texts.

Studies in health technology and informatics Jun 26, 2020
The automated detection of adverse events in medical records might be a cost-effective solution for patient safety management or pharmacovigilance. Our group proposed an information extraction algorithm (IEA) for detecting adverse events in neurosurg...
Information Storage and Retrieval Muscle Weakness Natural Language Processing Pharmacovigilance Humans Electronic Health Records
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Adverse drug event rates in pediatric pulmonary hypertension: a comparison of real-world data sources.

Journal of the American Medical Informatics Association : JAMIA Feb 1, 2020
OBJECTIVE: Real-world data (RWD) are increasingly used for pharmacoepidemiology and regulatory innovation. Our objective was to compare adverse drug event (ADE) rates determined from two RWD sources, electronic health records and administrative claim...
Insurance, Health Electronic Health Records Drug-Related Side Effects and Adverse Reactions Regression Analysis Humans Pharmacovigilance Hypertension, Pulmonary Child, Preschool Female Natural Language Processing Retrospective Studies Child Current Procedural Terminology Male
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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...
Humans Pharmacovigilance Adverse Drug Reaction Reporting Systems Neural Networks, Computer Drug-Related Side Effects and Adverse Reactions
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Deep neural networks ensemble for detecting medication mentions in tweets.

Journal of the American Medical Informatics Association : JAMIA Dec 1, 2019
OBJECTIVE: Twitter posts are now recognized as an important source of patient-generated data, providing unique insights into population health. A fundamental step toward incorporating Twitter data in pharmacoepidemiologic research is to automatically...
Natural Language Processing Pharmaceutical Preparations Pharmacovigilance Deep Learning Humans Neural Networks, Computer Social Media
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