Big data, artificial intelligence and machine learning in drug safety
Course content
The need for competencies in Pharmaceutical Data Science is steadily increasing in response to the explosion of available and complex data in biomedicine and related streams. The vast volume of data covers a diverse landscape from the properties of drug molecules over their biological mechanisms of action to individual patient data collected in clinical trials and healthcare settings. This course provides an overview of data science methods in the context of drug safety. The course is tailored for both academia and industry.
Topics of the course are:
- Pharmaceutical data science for drug safety
- Introduction to artificial intelligence, machine learning, and deep learning
- Introduction to the Science of "Big Data"
- Data sources and their characteristics, the possibilities for access
- Case studies of applications of artificial intelligence, machine learning, and deep learning in drug safety
- Regulatory and ethical aspects of using big data artificial intelligence in pharmaceutical science for drug safety
Master of Industrial Drug Development (MIND) - elective
The course is preapproved as an elective in the
Master Medicines Regulatory Affairs
(MRA) programme.
Upon completion of the course, students are expected to be able to:
Knowledge
- describe and explain the fundamentals of data science with a focus on pharmaceutical data science
- describe the roles of a pharmaceutical data scientist within the wider pharmaceutical research environment
- describe and explain the key sources of health data, and the context in which these data are collected, implications of the context on issues such as data quality, accessibility, bias, and the appropriateness of use to address specific research questions
- describe and explain key issues related to ethics, data security, confidentiality and information governance
Skills
- discuss different analytical approaches
- discuss limitations of data sources and methods
- discuss the results of scientific studies and other information obtained using big data and data science methods
- discuss ethical, legal and regulatory aspects of big data and artificial intelligence
Competencies
- understand the fundamentals of data science with a focus on pharmaceutical data science
- understand the roles of a pharmaceutical data scientist within the wider pharmaceutical research environment
- interpret and critically assess scientific studies and other types of information produced using big data and data science methods
-
reflect on ethical, legal and regulatory aspects of big data and data science
Online part: E-lessons that will introduce you to basic concepts
of big data, artificial intelligence and machine learning. The
e-lessons are equivalent to one full course day.
On campus part:
Lectures, theory exercises including group work with real and
simulated scenarios.
Self-study of course literature.
Selected textbook chapters, lecture notes, laws, documents, recommendations, circulars, guidelines, and scientific papers.
Registered students register via the self-service on
KUnet.
New students apply
via this link:
Big Data, Artificial Intelligence and Machine Learning in Drug
Safety – University of Copenhagen
- ECTS
- 5 ECTS
- Type of assessment
-
Home assignment
- Type of assessment details
- The set assignment has two parts:
1. A case study that is presented with a short description and/or a scientific publication. The student is expected to identify key information, analyse the case study, critically assess data, methods and results, by answering a series of questions.
2. Short questions covering different topics of the course. - Aid
- All aids allowed
Read more about the GAI-rules: Generative AI and good academic practice in UCPH's education programmes – University of Copenhagen
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
- Exam period
-
See exam schedule.
- Re-exam
-
Same as ordinary.
Criteria for exam assessment
To achieve grade 12 the student must be able to:
Knowledge
- describe and explain the fundamentals of data science with a focus on pharmaceutical data science
- describe the roles of a pharmaceutical data scientist within the wider pharmaceutical research environment
- describe and explain the key sources of health data, and the context in which these data are collected, implications of the context on issues such as data quality, accessibility, bias, and the appropriateness of use to address specific research questions
- describe and explain key issues related to ethics, data security, confidentiality and information governance
Skills
- discuss different analytical approaches
- discuss limitations of data sources and methods
- discuss the results of scientific studies and other information obtained using big data and data science methods
- discuss ethical, legal and regulatory aspects of big data and artificial intelligence
Competencies
- understand the fundamentals of data science with a focus on pharmaceutical data science
- understand the roles of a pharmaceutical data scientist within the wider pharmaceutical research environment
- interpret and critically assess scientific studies and other types of information produced using big data and data science methods
-
reflect on ethical, legal and regulatory aspects of big data and data science
Part time Master courses
- Category
- Hours
- Lectures
- 20
- Preparation
- 88
- Theory exercises
- 15
- Exam
- 15
- English
- 138
Kursusinformation
- Language
- English
- Course number
- SMIM22002U
- ECTS
- 5 ECTS
- Programme level
- Part Time Master
- Placement
- Spring
A 6 day course taken over a period of 3 weeks
- Price
-
Fees are published on the course webpage at Big Data, Artificial Intelligence and Machine Learning in Drug Safety – University of Copenhagen. Fee includes course materials and lunch/coffee. Prices may be subject to change.
- Schedulegroup
-
This is a blended learning course.
Online Part - 1 week duration but the workload is equivalent to one course day
On Campus Part - one week with workload equivalent to 5 course days
-----
Combining one week of online learning, which is equivalent to one full course day and one week of on-campus/classroom-based learning.
See course calendar - Capacity
- 25 participants
- Studyboard
- Study Board for the Professionel Master´s Degree Programmes at The Faculty og Health and Medical Science
Contracting department
- Department of Drug Design and Pharmacology
Contracting faculty
- Faculty of Health and Medical Sciences
Course Coordinators
- Maurizio Sessa
(14-53677b786f806f7534796b79796746797b746a34717b346a71)
Main Course Responsible - Morten Andersen
(15-7476797b6c753568756b6c797a6c75477a7c756b35727c356b72)
Co Course Responsible
Co-course responsible: Morten Andersen
Teacher
Maurizio Sessa (Course Responsible)
Morten Andersen (Co-Course Responsible)
(professionals from safety/epidemiology/pharmacovigilance
departments in the pharmaceutical industry, regulatory agencies,
CROs to be selected)
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