Event
Natural Language Processing innovations for drug safety and vigilance
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September 5, 2023

Kalender
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11:00am

GMT

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Safety in drug development is one of the biggest challenges and opportunities facing life sciences companies today. The rapidly increasing volume of safety reports and relevant documents is almost unsustainable for manual efforts, and pharma companies are looking for innovative AI technologies to bring significant process improvements.

Many of our customers use the power of IQVIA’s Natural Language Processing (NLP) platform to optimize their safety processes such as medical literature monitoring, to reduce costs and gain more value from the insights generated.

NLP is an AI technology that transforms unstructured text into structured data, enabling rapid review and analysis. This capability can be applied for safety assessment and medical review, providing effective search of literature, drug labels and regulatory review packages for adverse events and the critical context around these, to help with a deeper understanding and contextualization of any potential safety signal.

This webinar will present an overview of customer success stories, including use cases from the FDA, and a demo of IQVIA’s NLP Safety Intelligence Hub, to show how NLP can enable your team to advance drug safety.

What will you learn?

  • How natural language processing (NLP) text mining can extract structured data from unstructured text for medical literature monitoring, safety assessment, contextualisation of safety signals, safety case processing, MedDRA mapping.
  • How big pharma access internal data silos and external data sources for safety decision making. Use cases from top pharma and the FDA will be discussed. Who should attend?
  • Teams involved in safety assessment, medical review, adverse event medical coding, safety systems, medical literature mining, pharmacovigilance, safety intelligence.
  • Teams with a need to get better value from both internal and external textual safety information and integrating diverse data sets to provide knowledge relevant to drug safety and risk prediction.
  • Informaticians, information professionals, researchers, with responsibility for:

    • Risk profiles for targets in early drug discovery
    • Preclinical and clinical drug safety
    • Safety assessment across the pipeline

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