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Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
Big data, machine learning and artificial intelligence: a neurologist's guide
Stephen D Auger1, Benjamin M Jacobs2,3, Ruth Dobson2,3
1Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, UK stephen.auger1@nhs.net.
Abstract:
Modern clinical practice requires the integration and interpretation of ever-expanding volumes of clinical data. There is, therefore, an imperative to develop efficient ways to process and understand these large amounts of data. Neurologists work to understand the function of biological neural networks, but artificial neural networks and other forms of machine learning algorithm are likely to be increasingly encountered in clinical practice. As their use increases, clinicians will need to understand the basic principles and common types of algorithm. We aim to provide a coherent introduction to this jargon-heavy subject and equip neurologists with the tools to understand, critically appraise and apply insights from this burgeoning field.
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