Advanced Applied Health Data Science module (GM52058)

An integral component of health data science is analysing and interpreting health data using information from electronic medical records (EMRs).

We will use real-world health data collected in the Tayside region. It is accessed in a trusted research environment (TRE). This protects patient privacy while still allowing important research.

In this module, you will explore linking, visualising, and analysing electronic healthcare records with genomic data. This will be done using the R statistical software and other bioinformatic tools.

What you will learn

In this module, you will:

• learn which statistical methods should be applied to electronic health data linked to genetic data
• learn about genome-wide association studies (GWAS)
• explore the theory and gain hands-on experience with real-time genetic data derived from the GoDARTS cohort
• explore the role of precision medicine in the healthcare industry
• analyse drug outcomes using genetic data
• gain experience handling real-world omics data

By the end of this module, you will be able to:

• explain the concepts of linear, logistic and generalised linear regression methods
• explain and interpret the coefficients and standard error outputs of single or multiple-predictor regression model estimations, including genomics
• understand how genetics can be used to discover the factors that affect human health
• learn how EMRs and genetics are influencing the development of new drugs

Assignments / assessment

• 2,000-word research paper (100%)
  • The assignment will be given in Week 1 and due in Week 11

This module does not have a final exam.

Teaching methods / timetable

• lectures
• hands-on sessions with case studies
• practical classes
  • learn how to interpret the adjusted and unadjusted genomic regression models with effect estimates
  • performing a survival analysis
  • handling genomic and transcriptomic data
• guest lectures by speakers from the healthcare industry