The use of machine learning to improve prognostic and diagnostic accuracy has been increasing at the expense of classic statistical models. In this talk Dr Lauric Ferrat presents results comparing the prediction performance of several well-known machine learning approaches to logistic regression. He then argues that focus should not be made on performance optimisation but clinical utility and ease of model access.
Lauric Ferrat is an applied mathematician. His research focuses on machine learning and statistical tools to build predictive model of autoimmune diseases. He completed his engineering diploma at the National School for Statistics and Information Analysis in France, specialising in advanced statistical engineering. He then undertook a PhD and then a Postdoc at the university of Exeter. He is currently carry on his research as a consultant for the university of Exeter and the University of Washington in Seattle.
Prediction Modelling Group
The Prediction Modelling Group provides a forum for researchers at King's College London’s Institute of Psychiatry, Psychology & Neuroscience (IoPPN) and clinicians at South London and Maudsley NHS Foundation Trust, who are interested in prediction modelling applications for precision medicine.
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