Undergraduate Course: Medical Informatics (MBCH08012)
Course Outline
| School | Edinburgh Medical School |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 8 (Year 2 Undergraduate) |
| Course type | Online Distance Learning |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Medicine is now a data-intensive discipline, with increasing amounts of data becoming available from research and practice. There is an opportunity, but also a challenge, to collect, represent and interpret such data to drive medical innovation. |
| Course description |
Medicine is now a data-intensive discipline, with increasing amounts of data becoming available from research and practice. There is an opportunity, but also a challenge, to collect, represent and interpret such data to drive medical innovation.
This course provides an introduction to data science in medicine, and more particularly to representing and interpreting data from areas across biomedicine and healthcare. It covers relational databases for medicine and healthcare, medical ontologies, statistical analysis of biomedical data, as well as some advanced topics in medical informatics, such as healthcare workflows and precision medicine. Students will learn the different perspectives from which biomedical data is used and the principles underlying a range of data models. They will also get practical experience in using current data science tools and applying a number of representation and manipulation methods to appropriate synthetic biomedical data sets.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate knowledge of the terminology and paradigms used in different areas of medical informatics for representing and interpreting data, by being able to apply them to sample data-intensive medical problems.
- Demonstrate understanding of different representations of biomedical data.
- Demonstrate knowledge of the basic techniques for interpreting and processing biomedical data, by being able to demonstrate how these techniques work for synthetic data sets.
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Reading List
There is no single compulsory course text. Pointers to appropriate material from the following textbooks will be made available online:
¿ Raghu Ramakrishnan and Johannes Gehrke. Database Management Systems. McGraw-Hill, 3rd edition, 2003.
¿ S. Sumathi and S. Esakkirajan. Fundamentals of relational database management systems. Springer, 2007.
¿ Dean Allemang and Jim Hendler. Semantic Web for the Working Ontologist: Effective Modelling in RDFS and OWL. Morgan Kaufmann, 2nd edition, 2011.
¿ Tom Heath and Christian Bizer. Linked Data: Evolving the Web into a Global Data Space. Morgan & Claypool Publishers, 2011.
¿ Daniel Navarro. Learning statistics with R: A tutorial for psychology students and other beginners. University of Adelaide, Version 0.5, 2015.
¿ Robert H. Riffenburgh. Statistics in medicine. Elsevier, 3rd edition, 2012. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Data intensive medicine,healthcare,healthcare databases,medical ontologies,biomedical stats |
Contacts
| Course organiser | Dr Areti Manataki
Tel: (0131 6)51 7894
Email: |
Course secretary | Mrs Julie Prentice
Tel: (0131) 242 6531
Email: |
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