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Research project: Feature extraction in clinical data

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Data mining techniques commonly used in engineering are being applied to continuously monitored human bio-indicators to predict health state.

Measuring microvascular in the clinic
Measuring microvascular

It has become increasingly possible to monitor a large number of parameters in both experimental and real-time systems. Often this data is used to determine the health of a system at a given point in time and, increasingly, to predict when the system will degrade or fail. When records are kept of such data from a large number of examples of similar systems it becomes possible to post process the data and classify these examples by their current state and past history to improve predictions of their future state.

How we find patterns in this data?

The aim of this project is to use computational intelligence techniques, for example clustering and particle swarm optimisation, to characterise and cluster data from free-living studies of human volunteers with chronic disease such as diabetes, metabolic syndrome and obesity. The project is investigating the relationships between features in continually measured parameters (e.g. blood sugar level profiles and physical activity patterns) and indicators of health state (e.g. microvascular function and long-term average blood sugar level) measured in a clinic or laboratory. The aim is to determine if patterns detectable within this data are reliable indicators of the health state of an individual.

Associated research themes

Bioengineering and human factors

Related research groups

Bioengineering Science
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