Principal Investigator: Dr Christopher Long
Siemens HealthCare GmbH, Erlangen, GermanyTags: 36681, causal inference, Machine Learning, matched-sampling, observational-studies
According to the World Health Organisation (2012), neurological disease in its various forms afflicts tens of millions of people worldwide and in some of its domains is forecast to rise significantly. In prevalence studies of Alzheimers Disease (AD) for example, figures are predicted to rise from around 36 million today to over 100 million by 2050. While there are limited treatment options available for many of these afflictions, it is likely that future treatment strategies will be most effective if applied at the earlier stages of disease.
It is the aim of this one year project to increase the clinical utility of large-scale neuroimaging datasets through improved statistical modelling of underlying disease factors as they relate to neurological disease. With the ultimate goal of developing a statistical framework for assessing different treatment regimens, we seek both to enhance early disease detection and advance understanding of the complex neurological mechanisms that foreshadow disease onset.