Artificial intelligence and Big Data for Pharmacoepidemiology

Aim and content

This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH.

Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline, available seats will be allocated to the waiting list.

The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at NorDoc member faculties. All other participants must pay the course fee.

Learning objectives

A student who has met the objectives of the course will be able to:

1. Acquire knowledge of data mining and data integration methodologies for extracting valuable insights from diverse data sources, such as electronic health records

2. Gain insights into the common methodological flaws encountered when applying AI in pharmacoepidemiology studies, and learn how to recognize them.

3. Learn about best pramitigation strategies to overcome methodological challenges in AI-based pharmacoepidemiology studies, with a focus on enhancing data quality, addressing confounding, and ensuring robust study design.

Content

Course Description: The PhD course “Artificial Intelligence and Big Data in Pharmacoepidemiology” provides an in-depth exploration of the applications of artificial intelligence (AI) and big data methodologies in the field of pharmacoepidemiology. The course aims to equip students with knowledge and skills to harness the power of AI and big data in studying drug safety in the context of population-based studies.

Curriculum and Content:

Fundamentals of Artificial Intelligence and Big Data:
• Introduction to artificial intelligence and machine learning techniques
• Understanding big data and its characteristics in healthcare
• Data preprocessing and feature selection techniques

Identifying methodological flaws:
• Gain insights into the common methodological flaws encountered when applying AI in pharmacoepidemiology studies
Learn how to recognize and address them effectively.

Case Studies and Practical Applications:

• Analysis of real-world or synthetic/simulated datasets using AI and big data techniques
• Interpretation of findings and critical evaluation of results
• Integration of AI and big data methodologies into pharmacoepidemiological research

Throughout the course, students will engage in interactive lectures, discussions, and hands-on exercises to reinforce their understanding of the concepts and techniques. They will also have the opportunity to work on practical projects and case studies to apply AI and big data methodologies in pharmacoepidemiology research.

By the end of the course, students will have gained a comprehensive understanding of the principles, methodologies, and ethical considerations related to the use of AI and big data in pharmacoepidemiology. They will be equipped with the necessary skills to undertake advanced research and contribute to advancements in drug safety and public health.

Participants

The maximum number of participants is 20. The course is open for PhD students and junior researchers. Students from outside the Nordic countries can also apply. All applicants must have basic and documented skills in using statistical software (SAS, Stata, or R) for data management, and have – at a minimum – basic level knowledge in epidemiology and statistics. Blended Learning Approach: By providing access to video lectures prior to the course, participants can familiarize themselves with the core concepts and essential knowledge at their own pace. This enables a deeper understanding of the subject matter and sets the stage for engaging discussions and practical exercises during the in-person session.

Recorded Sessions: We understand the value of flexibility and acknowledge that participants may have conflicting schedules. Therefore, all sessions will be recorded, ensuring that even if you are unable to attend the live event, you won’t miss out on the content. The recorded sessions will be made available for review, allowing you to revisit key topics and reinforce your learning experience.

Relevance to graduate programmes

The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH:

Pharmaceutical Sciences (Drug Research Academy)
Public Health and Epidemiology
Biostatistics and Bioinformatics

Language

The course will be in English

Form

The course will include lectures, group work, and exercise classes. It is compulsory to participate in individual work and group work. Any absence will have to be compensated by extra individual assignments outside the course periods and provided by the course organizers. The examination will be performed as an oral and written presentation of group work. Additionally, each student will be assessed individually.

Course director

Assoc. Prof. Maurizio Sessa (co-director), Pharmacovigilance Research Center, Department of Drug Design and Pharmacology, University of Copenhagen – maurizio.sessa@sund.ku.dk

Teachers

Assoc. Prof. Maurizio Sessa MPharm, PhD, Pharmacovigilance Research Center, Department of Drug Design and Pharmacology, University of Copenhagen – maurizio.sessa@sund.ku.dk

Prof. Anton Pottegård, University of Southern Denmark – apottegaard@health.sdu.dk

Dates

25 – 29 November 2024

Course location

TBA, PharmaSchool, Universitetsparken 2/Jagtvej 160, 2100 Copenhagen

Registration

Please register before 1 October 2024

Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrolment.

Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

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