The use of viscoelastic haemostatic assays in non-cardiac surgical settings. a systematic review and meta-analysis

Background: Thrombelastography (TEG) and rotational thromboelastometry (ROTEM) are viscoelastic haemostatic assays (VHA) which exploit the elastic properties of clotting blood. The aim of this systematic review and meta-analysis was to evaluate the usefulness of these tests in bleeding patients outside the cardiac surgical setting. Materials and methods: We searched the Cochrane Library, MEDLINE, EMBASE and SCOPUS. We also searched clinical trial registries for ongoing and unpublished studies, and checked reference lists to identify additional studies. Results: We found 4 randomised controlled trials (RCTs) that met our inclusion criteria with a total of 229 participants. The sample size was small (from 28 to 111 patients) and the follow-up periods very heterogenous (from 4 weeks to 3 years). Pooled data from the 3 trials reporting on mortality (199 participants) do not show any effect of the use of TEG on mortality as compared to standard monitoring (based on the average treatment effect from a fixed-effects model): Risk Ratio (RR) 0.71; 95% Confidence Interval (CI): 0.43 to 1.16. Likewise, the use of VHA does not reduce the need for red blood cells (mean difference -0.64; 95% CI: -1.51 to 0.23), platelet concentrates (mean difference -1.12; 95% CI: -3.25 to 1.02), and fresh frozen plasma (mean difference -0.91; 95% CI: -2.02 to 0.19) transfusion. The evidence on mortality and other outcomes was uncertain (very low-certainty evidence, down-graded due to risk of biases, imprecision, and inconsistency). Conclusions: Overall, the certainty of the evidence provided by the trials was too low for us to be certain of the benefits and harms of viscoelastic haemostatic assay in non-cardiac surgical settings. More, larger, and better-designed RCTs should be carried out in this area

Signed Distance-based Deep Memory Recommender

Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them. However, some of the existing recommendation models (e.g., matrix factorization) assume a linear relationship between the user and item. This approach limits the capacity of recommender systems, since the interactions between users and items in real-world applications are much more complex than the linear relationship. To overcome this limitation, in this paper, we design and propose a deep learning framework called Signed Distance-based Deep Memory Recommender, which captures non-linear relationships between users and items explicitly and implicitly, and work well in both general recommendation task and shopping basket-based recommendation task. Through an extensive empirical study on six real-world datasets in the two recommendation tasks, our proposed approach achieved significant improvement over ten state-of-the-art recommendation models

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Unravelling the dynamics of online ratings

Online product ratings are an immensely important source of information for consumers and accordingly a strong driver of commerce. Nonetheless, interpreting a particular rating in context can be very challenging. Ratings show significant variation over time, so understanding the reasons behind that variation is important for consumers, platform designers, and product creators. In this paper we contribute a set of tools and results that help shed light on the complexity of ratings dynamics. We consider multiple item types across multiple ratings platforms, and use a interpretable model to decompose ratings in a manner that facilitates comprehensibility. We show that the various kinds of dynamics observed in online ratings are largely understandable as a product of the nature of the ratings platform, the characteristics of the user population, known trends in ratings behavior, and the influence of recommendation systems. Taken together, these results provide a framework for both quantifying and interpreting the factors that drive the dynamics of online ratings.Published versio