Postgraduate Course: Medical Informatics (MCLM11028)
|School||Deanery of Molecular, Genetic and Population Health Sciences
||College||College of Medicine and Veterinary Medicine
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
|Course type||Online Distance Learning
||Availability||Not available to visiting students
|Summary||Only available to students of the Data Science, Technology and Innovation (DSTI) online distance learning programme (please see below* for alternatives).
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 datasets.
*The course is also available as MCLM11037 for non-DSTI students.
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
d. Hypothesis testing
4. Current topics in Medical Informatics, including precision medicine and healthcare workflow management
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| Only available to students of the Data Science, Technology and Innovation (DSTI) online distance learning programme .
Course Delivery Information
|Academic year 2018/19, Not available to visiting students (SS1)
||Block 1 (Sem 1)
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|Additional Information (Assessment)
||Assignment 1: Practical exercise and written report on the design, implementation and querying of a relational database for medicine and healthcare (35%)
Assignment 2: Practical exercise and written report on the representation and querying of semantic web medical data (30%)
Assignment 3: Practical exercise and written report on the statistical analysis of a biomedical dataset (35%)
||Students will receive formative feedback from the tutor on the tutorials and summative feedback from their marked practicals.
|No Exam Information
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.
|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.
|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.
|Keywords||Healthcare databases,medical ontologies,biomedical statistics
|Course organiser||Dr Areti Manataki
|Course secretary||Mrs Agapi Stylianidou
Tel: (0131 6)50 9989