Risk Estimation, Risk Communication - a Bluffers Guide
The study of diagnostic logic allows for further insight into the evaluation and treatment of patients, however uncertainties within the diagnosis and the consequences of inaccuracies can have implications for patient health. Recent developments have emphasized the utility of automatic diagnosis mechanisms as a way to better support medical practitioners, and trends in computational diagnostic models may signal a greater use of computational applications for diagnosis in the future. In particular, by using natural frequencies, Bayesian conditional probability modelling can be used to support clinical decision-making and inform diagnostic logic. We developed a Bayesian algorithm in form of a tool which attaches likelihood ratios to clinical risk indicators of giant-cell arteritis (GCA), symptoms, signs, investigation as well as epidemiology factors. The algorithm estimates an individual probability of having GCA in terms of an interval. Furthermore, the algorithm incorporates robust Bayesian analysis to handle uncertainties that stems from lack of data or ambiguity in data.
Louis Clearkin was schooled by Jesuits in Liverpool, then trained in medicine at The University of Sheffield and Trinity College Dublin. He trained in ophthalmology in Leeds, St Paul's Eye Hospital, Liverpool and was a Fellow at Moorfields, Harvard Medical School and Johns Hopkins Medical School Baltimore. He worked as Consultant Ophthalmologist for the Royal Commonwealth Society for the Blind in St Vincent, West Indies and at St John's Hospital, Jerusalem before consultant posts in South Yorkshire, Wirral and Liverpool. He worked as visiting faculty of ORBIS International, in India, Sudan, Mali, China and Haiti.
He has academic affiliated to The Ocular Biomechanics group and The Risk Institute at Liverpool University, is sometime Visiting Lecturer in Physic at Gresham College London and Leeds TARGET consortium.