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This module will introduce the main principles of health data science and epidemiology using electronic health records and provide an overview of the main research areas where these are applied. You will become familiar with medical ontologies, data linkage, precision medicine and phenotyping electronic health records for research. Additionally, you will be introduced to methods for processing health data in a data-driven manner such as machine learning, medical imaging, and natural language processing methods.

At the end of the module you will be able to:

1) Outline the main types of EHR data, and how medical ontologies are utilized to record healthcare information
2) Summarise the main advantages and limitations of creating and evaluating electronic health record phenotypes
3) Explain and practice the foundations of data visualisation
4) Summarize the importance of medical imaging and how data from medical images can be visualized and processed and used for research
5) Outline and apply the principles of natural language processing for health data, and explain the foundations of artificial intelligence in healthcare
6) Understand the fundamental concepts of epidemiology e.g. measures of frequency of disease, effect of association, and impact.
7) Explain the concepts of confounding, effect modification and bias in the context of epidemiological analyses
8) Outline how to measure incidence & prevalence, of disease
9) Explain the main types of observational studies (cohort, cross sectional, ecological)

Teaching on the module will be a combination of lectures, invited speakers, computer-based practicals and group work.