Big Data, AI, and Machine Learning in Drug Safety

How can data science methods contribute in securing drug safety?

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19 - 23 May 2025

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On location

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English

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27,000 DKK ex VAT

(app. 3,630 EUR ex VAT)

Summary

At this five day course you gain knowledge and insights you can translate into action in your role as a pharmacovigilance professional and responsible for taking advantage of the possibilities inherent in big data, artificial intelligence, and machine learning. For todays’s phamacovigilance professionals, it is necessary to be able to make use of the data science toolbox. But how do we choose the right data for our specific objective? And collect and process data in a way that considers both ethical, legal, and regulatory aspects of big data and data science?


At this course you get the answers to these questions and more, and you acquire a fundamental understanding of data science. On top of the theoretical understanding, we discuss concrete examples of how to use AI as well as examples of dos and don’ts. Prior to the 5 days physical course, there is an online part: the duration is 2 weeks, but the workload it is equivalent to one day.

This accredited course (ECTS 5) is offered in collaboration with University of Copenhagen.

    Keywords

    • Big data
    • Artificial Intelligence
    • Machine learning
    • Drug safety
    • Data science

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    Course leader & lecturers

    • Faisal M. Khan
      Course leader
      CVP, Advanced Analytics, AI & RWD
      Novo Nordisk A/S
    • Maurizio Sessa
      Course leader
      Assistant Professor of Pharmacoepidemiology
      Københavns Universitet
    • Morten Andersen
      Course leader
      Professor in Pharmacovigilance
      University of Copenhagen
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    Is this course for you?

    This course is relevant for all experienced pharmacovigilance professionals, e.g. safety surveillance advisers, safety scientists, patient safety associates, data science professionals, and employees of The Danish Medicine Agency.

    What you will learn

    1. To understand different analytical approaches, and the limitations of data sources and methods.
    2. To discuss the results of scientific studies and other information obtained using big data and data science methods.
    3. To interpret and critically assess scientific studies and other types of information produced using big data and data science methods.
    4. To discuss ethical, legal, and regulatory aspects of big data and artificial intelligence.

    What your company will get

    1. An employee who knows the fundamentals of data science, and how to translate them into practice.
    2. An employee who knows the key sources of health data and issues such as data quality, accessibility, and bias.
    3. An employee who understands the appropriateness of different data types to address specific research questions.
    4. An employee who understands key issues related to ethics, data security, confidentiality, and information governance.

    Course calendar

    Starting 19 May 2025
    Online module

    Intro webinar 9 May 2025
    15.00-16.00

    • What is artificial intelligence?
    • Artificial Intelligence vs. traditional statistics
    • Supervised vs. unsupervised learning
    • Prediction performance, errors, and cross validation 
    • Feature selection methods
    • cBioPortal, genetic & omics data  
    19 May 2025 9:00-16:00
    Day 1

    Fundamentals of data science and data sources

    • Artificial intelligence in drug safety.
    • Data quality and artificial intelligence.
    • Overview of artificial intelligence techniques in drug safety.
    • Big data in drug safety.
    • Workshop: how to identify, understand, and interpret artificial intelligence techniques in scientific articles.
    20 May 2025 9:00-16:00
    Day 2

    Artificial intelligence in Pharmacovigilance

    • An overview of artificial intelligence in pharmacovigilance.
    • Natural Language Processing (NLP) in case processing.
    • Machine learning in signal detection and validation.
    • Artificial intelligence for literature monitoring.
    • Workshop: artificial intelligence in pharmacovigilance.
    21 May 2025 9:00-16:00
    Day 3

    Artificial intelligence in Pharmacoepidemiology

    • An overview of artificial intelligence in pharmacoepidemiology.
    • Artificial intelligence for post-marketing surveillance using administrative/healthcare data.
    • Artificial intelligence for risk stratification.
    • Artificial intelligence for drug utilisation research.
    • Workshop: regulatory framework for artificial intelligence
    22 May 2025 9:00-16:00
    Day 4

    Artificial intelligence in other aspects of drug safety

    • Artificial intelligence for drug safety related aspects of drug discovery.
    • Artificial intelligence for toxicology.
    • Artificial intelligence for drug safety-related aspects of genetic/omics data.
    • Workshop: artificial intelligence for quality assurance in drug manufacturing.
    23 May 2025 9:00-16:00
    Day 5

    Artificial intelligence: regulatory aspects, ethics, data security, and confidentiality

    • The regulatory framework for artificial intelligence in drug safety.
    • Privacy and artificial intelligence.
    • Ethical aspects of artificial intelligence in drug safety.
    • Workshop: the regulatory framework for artificial intelligence.
    Practical information

    Registration

    Registration deadline
    7 Apr 2025
    Register
    19 - 23 May
    Sometimes things change. This is the expected programme.

    Course information

    Literature

    Prior to the course you get access to mandatory and/or optional readings via your personal Atrium log-in.

    Examination

    The exam is held online, usually 4-6 weeks after the course.

    You will receive a link with exam questions via your personal Atrium log-in.

    To participate in the exam, you must have attended the course.

    Course leaders

    Faisal M. Khan
    CVP, Advanced Analytics, AI & RWD
    Novo Nordisk A/S
    Maurizio Sessa
    Assistant Professor of Pharmacoepidemiology
    Københavns Universitet
    Morten Andersen
    Professor in Pharmacovigilance
    University of Copenhagen

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    Want to know more, or need help?

    Contact Educational Programme Leader Lone Rex at +45 20 62 11 46

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