Recent advances in causal machine learning and wider artificial intelligence (AI) methods could provide new insights into the natural histories and potential prevention of clusters of multiple long-term conditions or multimorbidity (MLTC-M). When combined with expertise in clinical practice, applied health research and social science, there is potential to systematically identify and map new clusters of disease, understand the trajectories of patients with these conditions throughout their life course, predict serious adverse outcomes, optimise therapies and consider the influence of wider determinants such as environmental, behavioural and psychosocial factors. The National Institute of Health Research (NIHR) recently funded multidisciplinary consortia to bring together AI specialists, experts in big data and MLTC-M in the first and second waves of this new programme. The so-called AIM consortia of researchers will spearhead the use of artificial intelligence methods and develop insights for the identification and subsequent prevention of MLTC-M. This consensus agreement is aimed at facilitating a community of learning within the AIM consortia, promoting cooperation, transparency and rigour in our approaches while maintaining high methodological standards and consistency in defining and reporting within our research. In bringing together these research collaborations, there is also an opportunity to foster shared learning, synergies and rapidly compare and validate new AI approaches across our respective studies. This step is critical to implementation on the pathway to patient and public benefit.
Scope and aimThis statement was developed by the first wave of the NIHR AIM consortia and received input from the second wave. It includes representatives across thirteen universities from Edinburgh,
This article considers the creation of an exhibition at the National Library of New Zealand. Specifically, this project was a collaborative endeavour between the School of Design at Victoria University of Wellington and the National Library, aimed at utilising new technologies and traditional archival research to bring forgotten and rarely accessed data back to life in a public exhibition. From the outset this project sought to combine the best of both new educational technologies (augmented reality) with tactile, physical materials (created using laser cutting and 3D printing) that the public could handle and use in a way that would break down the barriers between exhibition objects and resurrected archival data. The result was an interactive exhibition focused upon the emergence of Wellington as a city and the growth of its waterfront over the last century and a half. The design research project took place between November 2016 and February 2017 and resulted in a project exhibition open to the public running from May 2017 to February 2018. The principle supervisors were Leon Gurevitch and Tim Miller with a research team of three research assistants (Stefan Peacock, Alasdair Tarry and Louis ElwoodLeach).
Emerging technologies influence the shape of society, how we interact with the world, how we learn, how our activities create new knowledge and how we can recontextualise old knowledge in new ways.
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