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Related Concept Videos

Pharmacovigilance01:19

Pharmacovigilance

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Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Applying AI to Structured Real-World Data for Pharmacovigilance Purposes: Scoping Review.

Stella Dimitsaki1, Pantelis Natsiavas2, Marie-Christine Jaulent1

  • 1Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé - LIMICS, Inserm, Université Sorbonne Paris-Nord, Sorbonne Université, Paris, France.

Journal of Medical Internet Research
|January 3, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) applied to structured real-world data (RWD) shows promise for pharmacovigilance. Further research is needed in explainable AI and data preprocessing protocols for drug safety.

Keywords:
artificial intelligencedrug safetymachine learningpharmacovigilancereal-world datascoping review

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Area of Science:

  • Pharmacovigilance and Drug Safety
  • Artificial Intelligence in Healthcare
  • Real-World Data Analysis

Background:

  • Artificial intelligence (AI) applied to real-world data (RWD) is a promising paradigm for pharmacovigilance.
  • Current research often focuses on unstructured RWD, necessitating an investigation into AI applications on structured RWD.

Purpose of the Study:

  • To conduct a scoping review on the emerging use of AI on structured RWD for pharmacovigilance.
  • To identify trends and potential research gaps in this field.

Main Methods:

  • Scoping review based on PRISMA methodology.
  • Searched MEDLINE via PubMed for studies from January 2010 to January 2024.
  • Evaluated studies based on AI approaches, data preprocessing, clinical validation, and trustworthy AI criteria.

Main Results:

  • 36 studies were included, with a significant increase post-2019.
  • Most studies focused on adverse drug reaction detection using nonsymbolic AI, particularly random forest.
  • Electronic health records were the primary RWD source, often lacking interoperability; code availability and clinical validation were limited.

Conclusions:

  • AI with structured RWD is promising for drug safety but requires further exploration of explainable and causal AI.
  • Standardized RWD preprocessing protocols are needed.
  • Evaluation procedures for sensitive data require further investigation.