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. It is also open to
single course students who meet the admission criteria.
See course calender for course dates
Upon completion of the course, participants 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.
Applicants must meet the following criteria:
• A relevant bachelor degree or equivalent
• A minimum of 2 years of relevant job experience
• Proficiency in English
Application deadline is 8 weeks before the first day of
instruction.
Apply directly on the programme's
webpage
- ECTS
- 5 ECTS
- Type of assessment
-
Written assignment
- Type of assessment details
- The 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
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
- Exam period
-
Announced in the exam plan on the MIND homepage mind.ku.dk
- Re-exam
-
Announced in the exam plan on the MIND homepage mind.ku.dk
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 and Diploma 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 programme's homepage at https://mind.ku.dk/tuition/ Fee includes course materials and lunch/coffee. Prices may be subject to change.
- Schedulegroup
-
This is a blended learning course.
Online Part - 2 weeks duration but the workload is equivalent to one course day
On Campus Part - one week with workload equivalent to 5 course days
------
Combining two weeks 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 Coordinator
- Morten Andersen (15-7173767869723265726869767769724477797268326f7932686f)
Teacher
Morten Andersen
Maurizio Sessa
(professionals from safety/epidemiology/pharmacovigilance
departments in the pharmaceutical industry, regulatory agencies,
CROs to be selected)
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Courseinformation of students