Unsupervised machine learning of integrated health and social care data from the Macmillan Improving the Cancer Journey service in Glasgow

Background: Improving the Cancer Journey (ICJ) was launched in 2014 by Glasgow City Council and Macmillan Cancer Support. As part of routine service, data is collected on ICJ users including demographic and health information, results from holistic needs assessments and quality of life scores as measured by EQ-5D health status. There is also data on the number and type of referrals made and feedback from users on the overall service. By applying artificial intelligence and interactive visualization technologies to this data, we seek to improve service provision and optimize resource allocation.Method: An unsupervised machine-learning algorithm was deployed to cluster the data. The classical k-means algorithm was extended with the k-modes technique for categorical data, and the gap heuristic automatically identified the number of clusters. The resulting clusters are used to summarize complex data sets and produce three-dimensional visualizations of the data landscape. Furthermore, the traits of new ICJ clients are predicted by approximately matching their details to the nearest existing cluster center.Results: Cross-validation showed the model’s effectiveness over a wide range of traits. For example, the model can predict marital status, employment status and housing type with an accuracy between 2.4 to 4.8 times greater than random selection. One of the most interesting preliminary findings is that area deprivation (measured through Scottish Index of Multiple Deprivation-SIMD) is a better predictor of an ICJ client’s needs than primary diagnosis (cancer type).Conclusion: A key strength of this system is its ability to rapidly ingest new data on its own and derive new predictions from those data. This means the model can guide service provision by forecasting demand based on actual or hypothesized data. The aim is to provide intelligent person-centered recommendations. The machine-learning model described here is part of a prototype software tool currently under development for use by the cancer support community.Disclosure: Funded by Macmillan Cancer Support</p

WINGS: a WIde-field Nearby Galaxy-cluster Survey. I - Optical imaging

This is the first paper of a series that will present data and scientific results from the WINGS project, a wide-field, multiwavelength imaging and spectroscopic survey of galaxies in 77 nearby clusters. The sample was extracted from the ROSAT catalogs with constraints on the redshift (0.0420). The global goal of the WINGS project is the systematic study of the local cosmic variance of the cluster population and of the properties of cluster galaxies as a function of cluster properties and local environment. This data collection will allow to define a local 'Zero-Point' reference against which to gauge the cosmic evolution when compared to more distant clusters. The core of the project consists of wide-field optical imaging of the selected clusters in the B and V bands. We have also completed a multi-fiber, medium resolution spectroscopic survey for 51 of the clusters in the master sample. In addition, a NIR (JK) survey of ~50 clusters and an H_alpha + UV survey of some 10 clusters are presently ongoing, while a very-wide-field optical survey has also been programmed. In this paper we briefly outline the global objectives and the main characteristics of the WINGS project. Moreover, the observing strategy and the data reduction of the optical imaging survey (WINGS-OPT) are presented. We have achieved a photometric accuracy of ~0.025mag, reaching completeness to V~23.5. Field size and resolution (FWHM) span the absolute intervals (1.6-2.7)Mpc and (0.7-1.7)kpc, respectively, depending on the redshift and on the seeing. This allows the planned studies to get a valuable description of the local properties of clusters and galaxies in clusters.Comment: 24 pages, 15 figures, Accepted by Astronomy and Astrophysic

DEMON: a Proposal for a Satellite-Borne Experiment to study Dark Matter and Dark Energy

We outline a novel satellite mission concept, DEMON, aimed at advancing our comprehension of both dark matter and dark energy, taking full advantage of two complementary methods: weak lensing and the statistics of galaxy clusters. We intend to carry out a 5000 sqdeg combined IR, optical and X-ray survey with galaxies up to a redshift of z~2 in order to determine the shear correlation function. We will also find ~100000 galaxy clusters, making it the largest survey of this type to date. The DEMON spacecraft will comprise one IR/optical and eight X-ray telescopes, coupled to multiple cameras operating at different frequency bands. To a great extent, the technology employed has already been partially tested on ongoing missions, therefore ensuring improved reliability.Comment: 12 pages, 3 figures, accepted for publication in the SPIE conference proceeding

Geospatial Variation in Caesarean Delivery

Aim: The purpose of this study was to evaluate the variation in caesarean delivery rates across counties in Georgia and to determine whether county-level characteristics were associated with clusters. Design: This was a retrospective, observational study. Methods: Rates of primary and repeat caesarean by maternal county of residence were calculated for 2008 through 2012. Global Moran\u27s I (Spatial Autocorrelation) was used to identify geographic clustering. Characteristics of high and low-rate counties were compared using student\u27s t test and chi squared test. Results: Spatial analysis of both primary and repeat caesarean rate identified the presence of clusters (Moran\u27s I = 0.375; p \u3c .001). Counties in high-rate clusters had significantly lower access to midwives, more deliveries paid by Medicaid, higher proportion of births for women belonging to racial/ethnic minority groups and were more likely to be rural

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

Technical report about sustainable urban freight solutions, part 1 of

The XMM-LSS survey. Survey design and first results

We have designed a medium deep large area X-ray survey with XMM - the XMM Large Scale Structure survey, XMM-LSS - with the scope of extending the cosmological tests attempted using ROSAT cluster samples to two redshift bins between 0<z<1 while maintaining the precision of earlier studies. Two main goals have constrained the survey design: the evolutionary study of the cluster-cluster correlation function and of the cluster number density. The results are promising and, so far, in accordance with our predictions as to the survey sensitivity and cluster number density. The feasibility of the programme is demonstrated and further X-ray coverage is awaited in order to proceed with a truly significant statistical analysis. (Abridged)Comment: Published in Journal of Cosmology and Astroparticle Physic

Grape Growing

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Discovery and Strategic Partnership Group Concept Mapping: 2014-2015 Progress Report

In 2014, New York State received funding from the U.S. Department of Education, Office of Special Education and Rehabilitative Services to begin the NYS PROMISE (Promote the Readiness of Minors in Supplemental Security Income) research initiative. The goal of this initiative is to coordinate the system of support surrounding these youths to better catalyze their potential to transition from Supplemental Security Income (SSI) to a sustainable future of living and earning as independent adults. To guide strategy and support PROMISE priorities over the course of the initiative, NYS PROMISE convened the NYS PROMISE Steering Committee, comprised of appointed liaisons from agencies who are connected to the NYS PROMISE initiative. To support sustainable partnership development for greater progress and impact on the goals of NYS PROMISE, the Steering Committee engaged in a structured, time sensitive strategic planning and partnership framework development effort. To develop the elements of a prioritized strategy, the group used Group Concept Mapping (GCM), and constructed a visual framework, or concept map, that served as the basis for prioritization and strategy development throughout the process. The GCM approach employs a group process to capture individual contributions for consensus around a given topic, using a structured approach with a specific sequence of steps that support timely and consistent engagement in the process. GCM incorporates opinions and values, and presents the results in ways that are understandable and usable. 25 individuals from 8 member agencies took part in the concept map development, contributing elements in response to the following prompt: “To yield enduring individual outcomes, a viable system to support youth with disabilities in their transition from high school to successful adult lives needs to include…