Module overview
This third-year options module introduces students to the principles and analytical methods that underpin precision health, with a focus on modelling clinical risk and uncertainty using statistical and machine learning techniques. Building on foundational biomedical and computational knowledge, students will explore key health datasets, calibrated risk estimation models, and approaches for quantifying and communicating uncertainty in real-world healthcare settings. Through practical examples and state-of-the-art applications, they will learn how bias, missingness, and evidence quality affect model reliability and how uncertainty-aware methods support safe, informed decision-making in precision health.
Linked modules
This is a new module, it was presented to sub-committee, and then also to ECS Education Committee.