Effective disease prevention requires sharing of good quality knowledge about disease risk factors that can predict likely outcomes following a given health intervention. Prediction of outcomes is complex: the sheer number of potential measurements, their variability and quality range, the individualised ways factor data can interact, the non-linear effects of time and other constraints. This talk will introduce recent research on the analysis of outcomes in diabetes and diabetic retinopathy and how the outputs can be used to design better quality, more effective individualised interventions and diagnostic screening services.
Ed is Healthcare ICT Research Lead at the Diabetes Research Unit in Cardiff University’s School of Medicine. His medical research career began at the Genetic Recombination Laboratory, Imperial Cancer Research Fund under Stephen C. West, FRS. He then moved to the ICI-University Joint Laboratory and MRC CMHT in Leicester setting up a Cardiac Ion Channels group and authoring a three-volume, c.1500 page encyclopaedia on ion channel molecular physiology published by Academic Press. Throughout these years Ed used and developed cutting-edge IT and informatics tools. In 2000 he joined Amersham PLC at its Cardiff Research Centre to invent a ‘next generation’ informatics approach to drug effect recognition based on semantic representation and computation of complex cellular electrophysiological signals (detected by a multi-parameter sensor platform). Following closure of Amersham Research in the UK, Ed worked with colleagues in Computer Science at Cardiff University on a successful DTI Technology Programme bid called Healthcare@Home. In the same theme, he now has working group roles in the Continua Health Alliance (c.150 major companies designing and implementing a single global interoperability specification for a personal healthcare IT and device ecosystem). Recently Ed has focused with colleagues in Computer Science on specifying and building a generic inferential system for longitudinal outcomes analysis. This forms part of a comprehensive information-driven approach to disease prevention which has begun to be embedded in distributed computational workflow tools for medical researchers