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Pharmacovigilance

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Named Entity Recognition in Pubmed Abstracts for Pharmacovigilance Using Deep Learning.

Studies in health technology and informatics
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...

Supervised Machine Learning-Based Decision Support for Signal Validation Classification.

Drug safety
INTRODUCTION: Signal validation in pharmacovigilance is the process of evaluating data to decide whether evidence is sufficient to justify further assessment of a detected signal. During the signal validation process, safety experts in our organizati...

Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources.

Drug safety
With the rapid development of artificial intelligence (AI) technologies, and the large amount of pharmacovigilance-related data stored in an electronic manner, data-driven automatic methods need to be urgently applied to all aspects of pharmacovigila...

Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations.

Drug safety
Increasing availability of electronic health databases capturing real-world experiences with medical products has garnered much interest in their use for pharmacoepidemiologic and pharmacovigilance studies. The traditional practice of having numerous...

Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review.

Drug safety
INTRODUCTION: Artificial intelligence based on machine learning has made large advancements in many fields of science and medicine but its impact on pharmacovigilance is yet unclear.

Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance.

Drug safety
TransCelerate reports on the results of 2019, 2020, and 2021 member company (MC) surveys on the use of intelligent automation in pharmacovigilance processes. MCs increased the number and extent of implementation of intelligent automation solutions th...

Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations.

Drug safety
The tools of artificial intelligence (AI) have enormous potential to enhance activities in pharmacovigilance. Pharmacovigilance experts need not be AI experts, but they should know enough about AI to explore the possibilities of collaboration with th...

Artificial Intelligence and Machine Learning for Safe Medicines.

Drug safety
Authors' views on the role of artificial intelligence and machine learning in pharmacovigilance. (MP4  139807 kb).

Overcoming Major Barriers to Build Efficient Decision Support Systems in Pharmacovigilance.

Studies in health technology and informatics
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...

Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions.

Journal of biomedical informatics
The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, ADRs are a significant source of patient morbidity, ...