Undergraduate Course: Topics in Biomedical Informatics (INFR11263)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | The course will consist of a series of lectures and assisted discussions on biomedical innovation delivered by selected experts in the respective domains.
The course will cover a variety of biomedical topics, data types and their challenges/constraints along with state-of-the-art methodologies in biomedical informatics.
It aims to equip students with essential interdisciplinary skills to explore and discuss biomedical research and innovation challenges and critically assess analytic avenues to answer them. |
| Course description |
During the course, students will be exposed to a varied selection of biomedical research topics and data analytics approaches representative of current challenges in the biomedical domain. Sessions
will cover cutting-edge research problems that involve clinical and lab-collected data challenges including patient, omics and time-series data.
The topics will be delivered by experts in the respective biomedical application areas presenting a published work of biomedical analysis. In advance of each lecture, students will be provided with a
research article that will form the basis of the lecture and the follow-on discussion. Students will be required to study the article prior to the session, critically examining suitability of (a) data for the
research question at hand and (b) methodologies used for data analysis, as well as discuss (c) the conclusions drawn from the analysis.
Sessions will be interactive and students will be expected to actively participate in discussions, which will help them gain the skills that will be probed in the final course assessment.
Summative assessment will consist of an exam with the choice of answering two of three questions based on the content of three of the sessions presented during the semester. An additional
formative assessment will follow the same format as the exam but on a single presentation (typically the first in the series)
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Information for Visiting Students
| Pre-requisites | Same as other requirements |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2024/25, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 9,
Seminar/Tutorial Hours 9,
Feedback/Feedforward Hours 2,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
76 )
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| Assessment (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Written Exam _100_% |
| Feedback |
One session will be dedicated to an example topic. A practice assessment question will then be set with time to do this and submit. There will be a debrief in a class room session as well as individual feedback on submitted work. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Critically assess the challenges associated with biomedical data analysis and modelling across a variety of contexts, in particular with respect to noise in the data, patient stratification and regulatory and ethical issues.
- Critically discuss and compare data acquisition, analysis and modelling protocols.
- Present and explain biomedical data problems and appropriate analysis in one area of biomedicine to an interdisciplinary audience.
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Additional Information
| Graduate Attributes and Skills |
Research and enquiry: problem-solving, critical/analytical thinking, handling ambiguity, knowledge integration.
Personal responsibility and autonomy: ethics and social responsibility, independent learning, self-awareness and reflection, creativity, decision-making.
Communication: interpersonal/teamwork skills; verbal, written, and cross-disciplinary communication. |
| Keywords | Biomedical AI,Bioinformatics,Artificial Intelligence |
Contacts
| Course organiser | Dr Andrea Weisse
Tel: (0131 6)51 1211
Email: Andrea.Weisse@ed.ac.uk |
Course secretary | |
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