Effective UK weight management services for adults

A number of evidence-based weight management interventions are now available with different models, and serving different patient/client groups. While positive outcomes are key to the decision making process, so too is the information around how these outcomes were achieved, in what population, how transferable the outcomes would be to the population a service would be aiming to cover and at what cost to the service-provider and or the individual. This paper examines all the UK interventions with recent peer-reviewed evidence of their effectiveness in “realistic” settings and cost-effectiveness, in the context of NICE and SIGN guidelines. It concludes that the evidence-based approaches allow intervention at different stages in the disease-process of obesity which are effective in different settings. Self-referral to commercial agencies, by individuals with relatively low BMI and few medical complications is a reasonable first step. For more severely obese individuals (e.g. BMI >35kg/m2) requiring more medically complicated care, evidence is largely lacking for these services, but the community-based Counterweight Programme is effective and cost-effective in maintaining weight loss >5kg up to 2 years for 30-40% of attenders. For more complicated and resistant obesity, referral to a secondary care-based service can generate short-term weight loss, but 12 months data are unavailable

Generic Howard Thurman Center flyer

Generic flyer for the Howard Thurman Center with the center's hours of operation

Vital Sign Ontology

We introduce the Vital Sign Ontology (VSO), an extension of the Ontology for General Medical Science (OGMS) that covers the consensus human vital signs: blood pressure, body temperature, respiratory rate, and pulse rate. VSO provides a controlled structured vocabulary for describing vital sign measurement data, the processes of measuring vital signs, and the anatomical entities participating in such measurements. VSO is implemented in OWL-DL and follows OBO Foundry guidelines and best practices. If properly developed and extended, we believe the VSO will find applications for the EMR, clinical informatics, and medical device communities

Spatial Sign Correlation

A new robust correlation estimator based on the spatial sign covariance matrix (SSCM) is proposed. We derive its asymptotic distribution and influence function at elliptical distributions. Finite sample and robustness properties are studied and compared to other robust correlation estimators by means of numerical simulations.Comment: 20 pages, 7 figures, 2 table

Sign Stable Projections, Sign Cauchy Projections and Chi-Square Kernels

The method of stable random projections is popular for efficiently computing the Lp distances in high dimension (where 0<p<=2), using small space. Because it adopts nonadaptive linear projections, this method is naturally suitable when the data are collected in a dynamic streaming fashion (i.e., turnstile data streams). In this paper, we propose to use only the signs of the projected data and analyze the probability of collision (i.e., when the two signs differ). We derive a bound of the collision probability which is exact when p=2 and becomes less sharp when p moves away from 2. Interestingly, when p=1 (i.e., Cauchy random projections), we show that the probability of collision can be accurately approximated as functions of the chi-square similarity. For example, when the (un-normalized) data are binary, the maximum approximation error of the collision probability is smaller than 0.0192. In text and vision applications, the chi-square similarity is a popular measure for nonnegative data when the features are generated from histograms. Our experiments confirm that the proposed method is promising for large-scale learning applications

Sign language recognition with transformer networks

Sign languages are complex languages. Research into them is ongoing, supported by large video corpora of which only small parts are annotated. Sign language recognition can be used to speed up the annotation process of these corpora, in order to aid research into sign languages and sign language recognition. Previous research has approached sign language recognition in various ways, using feature extraction techniques or end-to-end deep learning. In this work, we apply a combination of feature extraction using OpenPose for human keypoint estimation and end-to-end feature learning with Convolutional Neural Networks. The proven multi-head attention mechanism used in transformers is applied to recognize isolated signs in the Flemish Sign Language corpus. Our proposed method significantly outperforms the previous state of the art of sign language recognition on the Flemish Sign Language corpus: we obtain an accuracy of 74.7% on a vocabulary of 100 classes. Our results will be implemented as a suggestion system for sign language corpus annotation