We adopt when necessary traditional data modelling and mining techniques.
We use the most advances techniques in Machine Learning, such as Restricted Boltzmann Machines (RBMs).
Convolutional neural networks (CNNs) are one of the most important methodologies in Deep Learning that have not yet been extensively applied to neuroimaging and cardiac imaging. Their application in computer vision has been hugely successful.
We will find the markers that can predict the rate of cognitive decline, which can lead to the identification and diagnosis Mild Cognitive Impairment (MCI) and dementia and the prediction of their onset.
We will tackle data extraction processes on large biomedical datasets to transform raw data into meaningful markers or imaging-derived phenotypes through advanced algorithms.
We will apply Data Mining, Machine Learning and Deep Learning methods, as well as some standard statistical techniques to advance our understanding of complex disease patterns and risk.
We will provide data-driven solutions for early prediction of diseases and their precise classification, share new findings and analysis tools with biomedical scientists, and help clinicians with decision-support software.
Because we know that software engineering is key to put in practice the insights and models we will develop in the programme, we aim to develop innovative solutions based on decision-support systems to help clinicians.
We are seeking academic colaborators and commercial partners.
If you are interested in being involved in our programme, don't hesitate to contact us.