Benchmarking machine learning models on multi-centre eICU critical care dataset

Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and public benchmarks. Recent availability of large clinical datasets has enabled the possibility of establishing public benchmarks. Taking advantage of this opportunity, we propose a public benchmark suite to address four areas of critical care, namely mortality prediction, estimation of length of stay, patient phenotyping and risk of decompensation. We define each task and compare the performance of both clinical models as well as baseline and deep learning models using eICU critical care dataset of around 73,000 patients. This is the first public benchmark on a multi-centre critical care dataset, comparing the performance of clinical gold standard with our predictive model. We also investigate the impact of numerical variables as well as handling of categorical variables on each of the defined tasks. The source code, detailing our methods and experiments is publicly available such that anyone can replicate our results and build upon our work.Comment: Source code to replicate the results https://github.com/mostafaalishahi/eICU_Benchmar

Community Development Evaluation Storymap and Legend

Community based organizations, funders, and intermediary organizations working in the community development field have a shared interest in building stronger organizations and stronger communities. Through evaluation these organizations can learn how their programs and activities contribute to the achievement of these goals, and how to improve their effectiveness and the well-being of their communities. Yet, evaluation is rarely seen as part of a non-judgemental organizational learning process. Instead, the term "evaluation" has often generated anxiety and confusion. The Community Development Storymap project is a response to those concerns.Illustrations found in this document were produced by Grove Consultants

Managing Optimism Biases in the Delivery of Large-Infrastructure Projects: A Corporate Performance Benchmarking Approach

Optimism bias has been a considerable challenge in the planning and delivery of public services, particularly infrastructure mega projects.  This has resulted in consistently underestimated costs and overestimated benefits, as well as delivery delays.  This paper explores whether innovative mechanisms of collecting and publicly disseminating information about the performance of government contractors on past projects can contribute to improving the success rate of future initiatives.  Drawing on international examples from North America, Europe and Asia, it is found that the production and dissemination of greater information through benchmarking does not on its own lead to reductions in the prevalence of optimism biases.  However, there is evidence that when combined with incentives built formally into government procurement processes that reward strong past performance, benchmarking can support improvements in the quality of project outputs.   &nbsp

Learning From the Community: Effective Financial Management Practices in the Arts

Provides financial management practices identified from a survey of directors at leading arts organizations, in order to understand how their practices could be used across the arts sector. Includes a framework for developing self-assessment tools

Internal report cluster 1: Urban freight innovations and solutions for sustainable deliveries (2/4)

Technical report about sustainable urban freight solutions, part 2 of

Internal report cluster 1: Urban freight innovations and solutions for sustainable deliveries (1/4)

Technical report about sustainable urban freight solutions, part 1 of