Measuring quality of care with routine data: avoiding confusion between performance indicators and health outcomes

Objective To investigate the impact of factors outside the control of primary care on performance indicators proposed as measures of the quality of primary care. Design Multiple regression analysis relating admission rates standardised for age and sex for asthma, diabetes, and epilepsy to socioeconomic population characteristics and to the supply of secondary care resources. Setting 90 family health services authorities in England, 1989-90 to 1994-5. Results At health authority level socioeconomic characteristics, health status, and secondary care supply factors explained 45% of the variation in admission rates for asthma, 33% for diabetes, and 55% for epilepsy. When health authorities were ranked, only four of the 10 with the highest age-sex standardised admission rates for asthma in 1994-5 remained in the top 10 when allowance was made for socioeconomic characteristics, health status, and secondary care supply factors. There was also substantial year to year variation in the rates. Conclusion Health outcomes should relate to crude rates of adverse events in the population. These give the best indication of the size of a health problem. Performance indicators, however, should relate to those aspects of care which can be altered by the staff whose performance is being measured

Rotation-Invariant Restricted Boltzmann Machine Using Shared Gradient Filters

Finding suitable features has been an essential problem in computer vision. We focus on Restricted Boltzmann Machines (RBMs), which, despite their versatility, cannot accommodate transformations that may occur in the scene. As a result, several approaches have been proposed that consider a set of transformations, which are used to either augment the training set or transform the actual learned filters. In this paper, we propose the Explicit Rotation-Invariant Restricted Boltzmann Machine, which exploits prior information coming from the dominant orientation of images. Our model extends the standard RBM, by adding a suitable number of weight matrices, associated with each dominant gradient. We show that our approach is able to learn rotation-invariant features, comparing it with the classic formulation of RBM on the MNIST benchmark dataset. Overall, requiring less hidden units, our method learns compact features, which are robust to rotations.Comment: 8 pages, 3 figures, 1 tabl

ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network

In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems, such as leaf segmentation (a multi-instance problem) and counting. Most of these algorithms need labelled data to learn a model for the task at hand. Despite the recent release of a few plant phenotyping datasets, large annotated plant image datasets for the purpose of training deep learning algorithms are lacking. One common approach to alleviate the lack of training data is dataset augmentation. Herein, we propose an alternative solution to dataset augmentation for plant phenotyping, creating artificial images of plants using generative neural networks. We propose the Arabidopsis Rosette Image Generator (through) Adversarial Network: a deep convolutional network that is able to generate synthetic rosette-shaped plants, inspired by DCGAN (a recent adversarial network model using convolutional layers). Specifically, we trained the network using A1, A2, and A4 of the CVPPP 2017 LCC dataset, containing Arabidopsis Thaliana plants. We show that our model is able to generate realistic 128x128 colour images of plants. We train our network conditioning on leaf count, such that it is possible to generate plants with a given number of leaves suitable, among others, for training regression based models. We propose a new Ax dataset of artificial plants images, obtained by our ARIGAN. We evaluate this new dataset using a state-of-the-art leaf counting algorithm, showing that the testing error is reduced when Ax is used as part of the training data.Comment: 8 pages, 6 figures, 1 table, ICCV CVPPP Workshop 201

A proposito di Michele Amari.

Copia digital. Madrid : Ministerio de Cultura. Subdirección General de Coordinación Bibliotecaria, 200

Systematic review of the use of financial incentives in treatments for obesity and overweight

Nine studies met the criteria for inclusion in this systematic review of randomized controlled trials of treatments for obesity and overweight involving the use of financial incentives, with reported follow-up of at least 1 year. All included trials were of behavioural obesity treatments. Justification of sample size and blinding procedure were not mentioned in any study. Attrition was well described in three studies and no study was analysed on an intention to treat basis. Participants were mostly women recruited through media advertisements. Mean age ranged from 35.7 to 52.8 years, and mean body mass index from 29.3 to 31.8 kg m−2. Results from meta-analysis showed no significant effect of use of financial incentives on weight loss or maintenance at 12 months and 18 months. Further sub-analysis by mode of delivery and amount of incentives although also non-statistically significant were suggestive of very weak trends in favour of use of amounts greater than 1.2% personal disposable income, rewards for behaviour change rather than for weight, rewards based on group performance rather than for individual performance and rewards delivered by non-psychologists rather than delivered by psychologists.The Health Services Research Unit is funded by the Chief Scientist Office of the Scottish Executive Health Department. The views expressed here are those of the authors. Alison Avenell is funded by a Career Scientist Award from the Chief Scientist Office of the Scottish Executive Health Departmen