Conversions of Relief: on the perception of depth in drawings

Of the many ways in which depth can be intimated in drawings, perspective has undoubtedly been one of the most frequently examined. But there is also an equally rich history associated with other forms of pictorial representation. Alternatives to perspective became particularly significant in the early twentieth century as artists and architects, intent on throwing off the conventions of their predecessors, looked to new ways of depicting depth. In architecture, this tendency was exemplified by Modernism’s preference for parallel projection – most notably axonometric and oblique. The use of these techniques gave architects the opportunity to convey a new and uniquely modern form of spatial expression. At once shallow and yet expansive, a key feature of these drawings was their ability to support perceptual ambiguity. This paper will consider the philosophy and science of vision, out of which these preoccupations emerged. In this context, the nineteenth-century discovery of stereopsis and the invention of the stereoscope will be used to illustrate the way in which attempts to test the limits of spatial perception led to an opening up of visual experience; and provided a definition of visual experience that could encompass the representational ambiguities later exploited by the early twentieth-century avant-garde.

Risk Modelling Framework for Emergency Hospital Readmission, Using Hospital Episode Statistics Inpatient Data

The objective of this study was to develop, test and benchmark a framework and a predictive risk model for hospital emergency readmission within 12 months. We performed the development using routinely collected Hospital Episode Statistics data covering inpatient hospital admissions in England. Three different timeframes were used for training, testing and benchmarking: 1999 to 2004, 2000 to 2005 and 2004 to 2009 financial years. Each timeframe includes 20% of all inpatients admitted within the trigger year. The comparisons were made using positive predictive value, sensitivity and specificity for different risk cut-offs, risk bands and top risk segments, together with the receiver operating characteristic curve. The constructed Bayes Point Machine using this feature selection framework produces a risk probability for each admitted patient, and it was validated for different timeframes, sub-populations and cut-off points. At risk cut-off of 50%, the positive predictive value was 69.3% to 73.7%, the specificity was 88.0% to 88.9% and sensitivity was 44.5% to 46.3% across different timeframes. Also, the area under the receiver operating characteristic curve was 73.0% to 74.3%. The developed framework and model performed considerably better than existing modelling approaches with high precision and moderate sensitivity.