32 research outputs found
Comparing Interrelationships Between Features and Embedding Methods for Multiple-View Fusion.
Briefing: improving children and young people's mental health services: local data insights from England, Scotland and Wales.
In this briefing, we present analysis from the Networked Data Lab (NDL). Led by the Health Foundation, the NDL is a collaborative network of local analytical teams across England, Scotland and Wales. These teams analysed local, linked data sources to explore trends in mental health presentations across primary, specialist and acute services. This briefing includes: a) background on the trends in mental health disorders among children and young people and existing pressures on services, as well as an overview of the main policies in place in England, Scotland, and Wales to improve children and young people's mental health b) findings from NDL partners: we examine trends and patterns of service use, including the use of primary care, specialist mental health care and acute services, along with differences by demographic and socioeconomic characteristics c) examples of how local NDL teams used linked data to improve services in their area d) insights for national and local policymakers
Using Manifold Embedding for Automatic Threat Detection: An Alternative Machine Learning Approach
Effects on mortality of shielding clinically extremely vulnerable patients in Liverpool, UK, during the COVID-19 pandemic
Objective This study evaluates the impact of England’s COVID-19 shielding programme on mortality in the City of Liverpool in North West England. Study Design Shielded and non-shielded people are compared using data from linked routine health records on all people registered with a general practitioner in Liverpool from April 2020 to June 2021. Methods A discrete time hazard model and interactions between the shielding status and the periods of higher risk of transmission are used to explore the effects of shielding across the major phases of the COVID-19 pandemic. Results Shielding was associated with a 34% reduction in the risk of dying (HR= 0.66, 95% CI 0.58 to 0.76) compared with a propensity-matched non-shielded group. Shielding appeared to reduce mortality during the first and third wave, but not the second wave, where shielding was not mandated by Government. The effects were similar for males and females, but more protective for those living in the least deprived areas of Liverpool. Conclusions It is likely that the shielding programme in Liverpool saved lives, although this seems to have been a little less effective in more deprived areas. A comprehensive programme of identifying vulnerable groups and providing them with advice and support is likely to be important for future respiratory virus pandemics. Additional support may be necessary for socioeconomically disadvantaged groups to avoid increased inequalities
Improving children and young people’s mental health services
Across the UK, the number of children and young people experiencing mental health problems is growing.
Mental health services are expanding, but not fast enough to meet rising needs, leaving many children and young people with limited or no support. Too little is known about who receives care and crucially, who doesn’t.
This briefing presents analysis from the Health Foundation’s Networked Data Lab (NDL) about children and young people’s mental health. The analysis from local teams across England, Scotland and Wales has highlighted three key areas for urgent investigation, to help ensure children and young people get the care they need. These are:
rapid increases in mental health prescribing and support provided by GPs
the prevalence of mental health problems among adolescent girls and young women
stark socioeconomic inequalities across the UK.
To inform national policy decisions and local service planning and delivery, the quality of data collection, analysis and the linkage of datasets across services and sectors need to be improved and used more effectively
Responding to COVID-19 in the Liverpool City Region: COVID-19: How Modelling is Contributing to the Merseyside Response
Multiple-Feature Spatiotemporal Segmentation of Moving Sequences using a Rule-based Approach
In this paper a novel two-stage architecture for object-based segmentation of moving sequences is proposed using multiple features such as motion, intensity and texture. The first stage locates perceptually meaningful objects using a hierarchy of single-feature segmentation processes. The second stage refines the boundaries of located objects using a combination of features according to a set of appropriate rules. Experimental results show that the proposed approach yields intuitively correct as well as accurate segmentations of moving sequences, which compare favourably with established state-of-the art techniques in the literature
METHOD AND APPARATUS FOR ORDERING IMAGE
A method, video apparatus, system and computer program product are disclosed. The method is for re-ordering images in a set of images. The method compress measuring for each image a feature value for each of a plurality of image features and determining over the set of images a correlation measure representing for at least some combinations of the image features the correlation in the respective feature values. The method then includes selecting in accordance with said correlation measure at least one closely correlated combination of image features and ordering the set of images in accordance with those closely correlated combinations of image features
R code for NDL report: Improving children and young people's mental health services
R code in GitHub rep