ORCID Profile
0000-0002-1452-9912
Current Organisations
University of Oxford
,
University of New South Wales
,
Queens University
,
CSIRO
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Elsevier BV
Date: 03-2017
Publisher: Elsevier BV
Date: 03-2015
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 23-07-2019
DOI: 10.2215/CJN.01530219
Abstract: Patients who have failed a transplant are at increased risk of repeat transplant failure. We determined access to transplantation and transplant outcomes in patients with and without a history of transplant failure. In this observational study of national data, the proportion of waitlisted patients and deceased donor transplant recipients with transplant failure was determined before and after the new kidney allocation system. Among patients initiating maintenance dialysis between May 1995 and December 2014, the likelihood of deceased donor transplantation was determined in patients with ( n =27,459) and without ( n =1,426,677) a history of transplant failure. Among transplant recipients, allograft survival, the duration of additional kidney replacement therapy required within 10 years of transplantation, and the association of transplantation versus dialysis with mortality was determined in patients with and without a history of transplant failure. The proportion of waitlist candidates (mean 14%) and transplant recipients (mean 12%) with transplant failure did not increase after the new kidney allocation system. Among patients initiating maintenance dialysis, transplant-failure patients had a higher likelihood of transplantation (hazard ratio [HR], 1.16 95% confidence interval [95% CI], 1.12 to 1.20 P .001). Among transplant recipients, transplant-failure patients had a higher likelihood of death-censored transplant failure (HR, 1.44 95% CI, 1.34 to 1.54 P .001) and a greater need for additional kidney replacement therapy required within 10 years after transplantation (mean, 9.0 95% CI, 5.4 to 12.6 versus mean, 2.1 95% CI, 1.5 to 2.7 months). The association of transplantation versus dialysis with mortality was clinically similar in waitlisted patients with (HR, 0.32 95% CI, 0.29 to 0.35 P .001) and without transplant failure (HR, 0.40 95% CI, 0.39 to 0.41 P .001). Transplant-failure patients initiating maintenance dialysis have a higher likelihood of transplantation than transplant-naïve patients. Despite inferior death-censored transplant survival, transplantation was associated with a similar reduction in the risk of death compared with treatment with dialysis in patients with and without a prior history of transplant failure.
Publisher: Elsevier BV
Date: 03-2022
Publisher: Elsevier BV
Date: 05-2016
Publisher: Copernicus GmbH
Date: 13-03-2018
DOI: 10.5194/HESS-22-1793-2018
Abstract: Abstract. In this study, information extracted from the first global urban fluvial flood risk data set (Aqueduct) is investigated and visualized to explore current and projected city-level flood impacts driven by urbanization and climate change. We use a novel adaption of the self-organizing map (SOM) method, an artificial neural network proficient at clustering, pattern extraction, and visualization of large, multi-dimensional data sets. Prevalent patterns of current relationships and anticipated changes over time in the nonlinearly-related environmental and social variables are presented, relating urban river flood impacts to socioeconomic development and changing hydrologic conditions. Comparisons are provided between 98 in idual cities. Output visualizations compare baseline and changing trends of city-specific exposures of population and property to river flooding, revealing relationships between the cities based on their relative map placements. Cities experiencing high (or low) baseline flood impacts on population and/or property that are expected to improve (or worsen), as a result of anticipated climate change and development, are identified and compared. This paper condenses and conveys large amounts of information through visual communication to accelerate the understanding of relationships between local urban conditions and global processes.
Publisher: MDPI AG
Date: 22-04-2022
Abstract: Time series data from environmental monitoring stations are often analysed with machine learning methods on an in idual basis, however recent advances in the machine learning field point to the advantages of incorporating multiple related time series from the same monitoring network within a ‘global’ model. This approach provides the opportunity for larger training data sets, allows information to be shared across the network, leading to greater generalisability, and can overcome issues encountered in the in idual time series, such as small datasets or missing data. We present a case study involving the analysis of 165 time series from groundwater monitoring wells in the Namoi region of Australia. Analyses of the multiple time series using a variety of different aggregations are compared and contrasted (with single time series, subsets, and all of the time series together), using variations of the multilayer perceptron (MLP), self-organizing map (SOM), long short-term memory (LSTM), and a recently developed LSTM extension (DeepAR) that incorporates autoregressive terms and handles multiple time series. The benefits, in terms of prediction performance, of these various approaches are investigated, and challenges such as differing measurement frequencies and variations in temporal patterns between the time series are discussed. We conclude with some discussion regarding recommendations and opportunities associated with using networks of environmental data to help inform future resource-related decision making.
Publisher: Elsevier BV
Date: 09-2020
Publisher: Elsevier BV
Date: 09-2020
DOI: 10.1111/AJT.15917
Publisher: American Medical Association (AMA)
Date: 04-2021
Publisher: Wiley
Date: 12-2020
DOI: 10.1111/INSR.12432
Publisher: Elsevier BV
Date: 12-2018
DOI: 10.1111/AJT.14871
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Stephanie Clark.