Health Information Science Seminar Series presented by Dr. Allan Tucker

In this talk Dr. Tucker will explore issues with different methods for collecting and modelling clinical data. He will briefly discuss the advantages and disadvantages of cross-sectional and longitudinal studies, and the modelling of these types of data with the chief aim of forecasting disease progression whilst discovering subclasses of disease based on temporal aspects: This will include novel algorithms for identifying disease subclasses based upon different disease trajectories and disease subclasses based upon different disease dynamics where the process is inherently non-stationary. Finally, Dr. Tucker will explore methods for integrating both cross-sectional and longitudinal data into probabilistic models that lever the advantages of both.

Biog: Dr Tucker's first degree was in Cognitive Science at Sheffield University, UK, where he became interested in models of brain function and human and animal behaviour. His other interests include learning models of time-series data in order to try and understand the underlying processes, with a focus on biological, clinical and ecological data. He received his Ph.D. at Birkbeck College, University of London sponsored by the Engineering and Physical Sciences Research Council; Honeywell Hi-Spec Solutions, UK; and Honeywell HTC, USA. As a Senior Lecturer at Brunel University London he leads the Intelligent Data Analytics Research group. His current research interests in health informatics include applications of machine learning for modelling high dimensional gene expression data, the use of latent variables to improve disease prediction & understanding, and personalised models of disease trajectories.