4,879 research outputs found
The PREVENT Study: Preventing hospital admissions attributable to gout
BackgroundGout is the most common form of inflammatory arthritis, affecting 1 in 40 people in the UK. Despite highly effective treatments, hospital admissions for gout flares have doubled in England over the last 20 years. Many of these admissions may have been prevented if optimal gout management had been delivered to patients.Objectives1. Describe the epidemiology of gout management in primary and secondary care in the UK.2. Develop an intervention package for implementation during hospitalisations for gout flares, with the aim of improving care and reducing hospitalisations.3. Implement and evaluate this intervention in people hospitalised for gout.MethodsI used population-level health datasets (CPRD, OpenSAFELY, NHS Digital Hospital Episode Statistics) to evaluate outcomes for people with incident gout diagnoses over a 20-year period. I used multivariable regression and survival modelling to analyse factors associated with outcomes, including: i) initiation of urate-lowering therapies (ULT); ii) attainment of serum urate targets; and iii) hospitalisations for gout flares.With extensive stakeholder input, I developed an evidence-based intervention package to optimise hospital gout care. This incorporated the findings of a systematic literature review and process mapping of the admitted patient journey in a cohort of hospitalised gout patients. My intervention consisted of a care pathway, based upon British (BSR), European (EULAR) and American (ACR) gout management guidelines, which encouraged ULT initiation prior to discharge, followed by a nurse-led, post-discharge review to facilitate handover to primary care. I implemented this intervention in patients hospitalised for gout flares at King’s College Hospital over a 12-month period, and evaluated outcomes including ULT initiation, urate target attainment and re-admission rates.ResultsIn the UK, between 2004 and 2020, I showed that only 29% of patients with gout were initiated on ULT within 12 months of diagnosis, while only 36% attained urate targets. No significant improvements in these outcomes were observed after publication of updated BSR and EULAR gout management guidelines. Comorbidities, including chronic kidney disease, heart failure and obesity, associated with increased odds of ULT initiation but decreased odds of attaining urate targets. For patients who were diagnosed with gout during the COVID-19 pandemic, I showed that ULT initiation improved modestly, relative to before the pandemic, while urate target attainment trends were similar. Underlying these trends was a 31% decrease in incident gout diagnoses in England during the first year of the pandemic.Using linked primary and secondary care data, I showed that the risk of hospitalisations for gout flares is greatest within the first 6 months after diagnosis. ULT initiation is associated with more hospitalisations for flares within the first 6 months of diagnosis, but a reduced risk of hospitalisations beyond 12 months; particularly when urate targets are attained.After process mapping the admitted patient journey and systematically appraising the evidence base, I developed and implemented a multi-faceted intervention at King’s College Hospital, with the aim of improving hospital gout care. Following implementation of this intervention, the proportion of hospitalised gout patients who initiated ULT increased from 49% to 92%; more patients achieved serum urate targets; and there were 38% fewer repeat hospitalisations for gout flares.ConclusionsAt a population level, ULT initiation and urate target attainment remain sub-optimal for people with gout in the UK, despite updated management guidelines. Initiation of ULT is associated with long-term reductions in hospitalisations for flares; however, only a minority of patients hospitalised for gout flares are initiated on ULT. After designing and implementing a strategy to optimise hospital gout care, over 90% of patients were initiated on ULT, urate target attainment improved, and repeat hospitalisations decreased. My findings suggest that improved primary-secondary care integration is essential if we are to reverse the epidemic of gout hospitalisations
On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse
This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new
experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse
Dataflow Programming and Acceleration of Computationally-Intensive Algorithms
The volume of unstructured textual information continues to grow due to recent technological advancements. This resulted in an exponential growth of information generated in various formats, including blogs, posts, social networking, and enterprise documents. Numerous Enterprise Architecture (EA) documents are also created daily, such as reports, contracts, agreements, frameworks, architecture requirements, designs, and operational guides. The processing and computation of this massive amount of unstructured information necessitate substantial computing capabilities and the implementation of new techniques. It is critical to manage this unstructured information through a centralized knowledge management platform. Knowledge management is the process of managing information within an organization. This involves creating, collecting, organizing, and storing information in a way that makes it easily accessible and usable. The research involved the development textual knowledge management system, and two use cases were considered for extracting textual knowledge from documents. The first case study focused on the safety-critical documents of a railway enterprise. Safety is of paramount importance in the railway industry. There are several EA documents including manuals, operational procedures, and technical guidelines that contain critical information. Digitalization of these documents is essential for analysing vast amounts of textual knowledge that exist in these documents to improve the safety and security of railway operations. A case study was conducted between the University of Huddersfield and the Railway Safety Standard Board (RSSB) to analyse EA safety documents using Natural language processing (NLP). A graphical user interface was developed that includes various document processing features such as semantic search, document mapping, text summarization, and visualization of key trends. For the second case study, open-source data was utilized, and textual knowledge was extracted. Several features were also developed, including kernel distribution, analysis offkey trends, and sentiment analysis of words (such as unique, positive, and negative) within the documents. Additionally, a heterogeneous framework was designed using CPU/GPU and FPGAs to analyse the computational performance of document mapping
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Insights into software development approaches: mining Q &A repositories
© 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Context: Software practitioners adopt approaches like DevOps, Scrum, and Waterfall for high-quality software development. However, limited research has been conducted on exploring software development approaches concerning practitioners’ discussions on Q &A forums. Objective: We conducted an empirical study to analyze developers’ discussions on Q &A forums to gain insights into software development approaches in practice. Method: We analyzed 13,903 developers’ posts across Stack Overflow (SO), Software Engineering Stack Exchange (SESE), and Project Management Stack Exchange (PMSE) forums. A mixed method approach, consisting of the topic modeling technique (i.e., Latent Dirichlet Allocation (LDA)) and qualitative analysis, is used to identify frequently discussed topics of software development approaches, trends (popular, difficult topics), and the challenges faced by practitioners in adopting different software development approaches. Findings: We identified 15 frequently mentioned software development approaches topics on Q &A sites and observed an increase in trends for the top-3 most difficult topics requiring more attention. Finally, our study identified 49 challenges faced by practitioners while deploying various software development approaches, and we subsequently created a thematic map to represent these findings. Conclusions: The study findings serve as a useful resource for practitioners to overcome challenges, stay informed about current trends, and ultimately improve the quality of software products they develop.Peer reviewe
Breaking Virtual Barriers : Investigating Virtual Reality for Enhanced Educational Engagement
Virtual reality (VR) is an innovative technology that has regained popularity in recent years. In the field of education, VR has been introduced as a tool to enhance learning experiences. This thesis presents an exploration of how VR is used from the context of educators and learners. The research employed a mixed-methods approach, including surveying and interviewing educators, and conducting empirical studies to examine engagement, usability, and user behaviour within VR. The results revealed educators are interested in using VR for a wide range of scenarios, including thought exercises, virtual field trips, and simulations. However, they face several barriers to incorporating VR into their practice, such as cost, lack of training, and technical challenges. A subsequent study found that virtual reality can no longer be assumed to be more engaging than desktop equivalents. This empirical study showed that engagement levels were similar in both VR and non-VR environments, suggesting that the novelty effect of VR may be less pronounced than previously assumed. A study against a VR mind mapping artifact, VERITAS, demonstrated that complex interactions are possible on low-cost VR devices, making VR accessible to educators and students. The analysis of user behaviour within this VR artifact showed that quantifiable strategies emerge, contributing to the understanding of how to design for collaborative VR experiences. This thesis provides insights into how the end-users in the education space perceive and use VR. The findings suggest that while educators are interested in using VR, they face barriers to adoption. The research highlights the need to design VR experiences, with understanding of existing pedagogy, that are engaging with careful thought applied to complex interactions, particularly for collaborative experiences. This research contributes to the understanding of the potential of VR in education and provides recommendations for educators and designers to enhance learning experiences using VR
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