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ABSTRACT / Avoiding emergency admissions is advantageous both to individual health and the overall efficiency of the healthcare system. In this talk I present a model which predicts the risk of an emergency admission for each person in Scotland with predictors derived from their electronic health records. This builds on an existing simpler model which has been in clinical use since 2012. Motivated by this application we study potential problems in the updating of a predictive score for a binary outcome when an existing predictive score forms part of the standard workflow. In this setting, the existing score induces an additional causative pathway which can lead to problems when the original score is replaced.
In addition, I will give a brief overview of my research interests and recent achievements in methodology, theory and practice.
BIO / Dr Sebastian Vollmer is Director of Data Study Groups and Theme lead of the Health Programme at the Alan Turing Institute in London as well as Associate Professor at the Departments of Mathematics and Statistics at the University of Warwick. He obtained his PhD in mathematics under the supervision of Prof Andrew Stuart and Prof Martin Hairer at the University of Warwick in 2013. Subsequently, he joined the University of Oxford as postdoctoral fellow of Prof Arnaud Doucet and Prof Yee Whye Teh working on Bayesian Inference for Big Data with Stochastic Gradient Markov Chain Monte Carlo, before his appointment as Departmental Lecturer in 2014.
Dr Sebastian Vollmer founded the Data Study Groups in December 2016 and now leads the executive team delivering the events. Due to high demand from both businesses and academics, he now runs three study groups per year at The Alan Turing Institute. He also established the Data Science for Social Good Programme in the UK - a 3 months long event training data scientists to tackle problems that really matter combining training and delivery instead of doing each one separately.