University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : Deanery of Molecular, Genetic and Population Health Sciences : Molecular and Clinical Medicine

Postgraduate Course: Medical Informatics (MCLM11037)

Course Outline
SchoolDeanery of Molecular, Genetic and Population Health Sciences CollegeCollege of Medicine and Veterinary Medicine
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryMedicine 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 datasets.
Course description The course covers:
1. Relational databases for medicine and healthcare
a. Design & Representation: ER model, relational model
b. Querying: SQL
2. Medical ontologies
a. Concepts: metadata, ontologies, linked data
b. Representation: RDF
c. Querying: SPARQL
3. Statistical analysis of biomedical data
a. Data scales
b. Summary statistics
c. Visualisation
d. Hypothesis testing
4. Current topics in Medical Informatics, including precision medicine and healthcare workflow management
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2017/18, Not available to visiting students (SS1) Quota:  None
Course Start Block 1 (Sem 1)
Course Start Date 18/09/2017
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 20, Seminar/Tutorial Hours 10, Online Activities 1, Summative Assessment Hours 40, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 27 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Feedback Students will receive formative feedback from the tutor on the tutorials and summative feedback from their marked practicals.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. 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.
  2. Demonstrate understanding of different representations of biomedical data.
  3. 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.
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 Students will be able to work independently and critically, informed by knowledge of key concepts and principles in Medical Informatics. They will get practical experience in representing and manipulating biomedical data, allowing them to develop both technical skills in data science and communication skills when interpreting their findings.
KeywordsHealthcare databases,medical ontologies,biomedical statistics
Course organiserDr Areti Manataki
Course secretaryMr Fraser Pullar
Tel: (0131 6)51 7890
Help & Information
Search DPTs and Courses
Degree Programmes
Browse DPTs
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Important Information