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Description
Increasing access to large amounts of complex heterogeneous data during planning, delivery and post-operative management, paves the way for developing analytics tools and solutions to support decision-making and quality improvement in interventional medicine. This module will cover a) the key principles and techniques of data collection, fusion, analysis and visualisation of diverse data types and b) the theoretical basis of statistical inference, data mining and pattern analysis building a comprehensive view on the modelling and decision-making processes in surgical data science. The module will encompass a series of lectures and hands-on programming sessions, in which students will apply data science methods on surgical/interventional tasks, including decision support, workflow analysis and skill assessment, and be exposed on the limitations and challenges of developing analytics tools. The acquired skills are transferable to other areas of biomedical engineering (e.g. human-computer interaction, machine learning, robotics) as well as to domains beyond medicine. The module is suitable for students with a STEM background and basic knowledge of scripting programming.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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