Pressure profile analysis at hemodialysis needle: a new method for early detecion of vascular access stenoses

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The accuracy of volume flow measurements derived from pulsed wave Doppler: a study in the complex setting of forearm vascular access for hemodialysis

Purpose: Maturation of an arterio-venous fistula (AVF) frequently fails, with low postoperative fistula flow as a prognostic marker for this event. As pulsed wave Doppler (PWD) is commonly used to assess volume flow, we studied the accuracy of this measurement in the setting of a radio-cephalic AVF. Methods: As in-vivo validation of fistula flow measurements is cumbersome, we performed simulations, integrating computational fluid dynamics with an ultrasound (US) simulator. Flow in the arm was calculated, based on a patient-specific model of the arm vasculature pre and post AVF creation. Next, raw ultrasound signals were simulated, from which the Doppler spectra were calculated in both a proximal (brachial) and a distal (radial) location. Results: The velocity component in the direction of the US beam, in a centred, small, sample volume, can be captured accurately using PWD spectrum mean-tracking. However, deriving flow rate from these measurements is prone to errors: (i) the angle-correction which is influenced by the radial velocity components in the complex flow field; (ii) the largest error is introduced due to a lack of knowledge on the spatial flow profile

Semantics-driven event clustering in Twitter feeds

Detecting events using social media such as Twitter has many useful applications in real-life situations. Many algorithms which all use different information sources - either textual, temporal, geographic or community features - have been developed to achieve this task. Semantic information is often added at the end of the event detection to classify events into semantic topics. But semantic information can also be used to drive the actual event detection, which is less covered by academic research. We therefore supplemented an existing baseline event clustering algorithm with semantic information about the tweets in order to improve its performance. This paper lays out the details of the semantics-driven event clustering algorithms developed, discusses a novel method to aid in the creation of a ground truth for event detection purposes, and analyses how well the algorithms improve over baseline. We find that assigning semantic information to every individual tweet results in just a worse performance in F1 measure compared to baseline. If however semantics are assigned on a coarser, hashtag level the improvement over baseline is substantial and significant in both precision and recall

Modeling and predicting the popularity of online news based on temporal and content-related features

As the market of globally available online news is large and still growing, there is a strong competition between online publishers in order to reach the largest possible audience. Therefore an intelligent online publishing strategy is of the highest importance to publishers. A prerequisite for being able to optimize any online strategy, is to have trustworthy predictions of how popular new online content may become. This paper presents a novel methodology to model and predict the popularity of online news. We first introduce a new strategy and mathematical model to capture view patterns of online news. After a thorough analysis of such view patterns, we show that well-chosen base functions lead to suitable models, and show how the influence of day versus night on the total view patterns can be taken into account to further increase the accuracy, without leading to more complex models. Second, we turn to the prediction of future popularity, given recently published content. By means of a new real-world dataset, we show that the combination of features related to content, meta-data, and the temporal behavior leads to significantly improved predictions, compared to existing approaches which only consider features based on the historical popularity of the considered articles. Whereas traditionally linear regression is used for the application under study, we show that the more expressive gradient tree boosting method proves beneficial for predicting news popularity

Representation learning for very short texts using weighted word embedding aggregation

Short text messages such as tweets are very noisy and sparse in their use of vocabulary. Traditional textual representations, such as tf-idf, have difficulty grasping the semantic meaning of such texts, which is important in applications such as event detection, opinion mining, news recommendation, etc. We constructed a method based on semantic word embeddings and frequency information to arrive at low-dimensional representations for short texts designed to capture semantic similarity. For this purpose we designed a weight-based model and a learning procedure based on a novel median-based loss function. This paper discusses the details of our model and the optimization methods, together with the experimental results on both Wikipedia and Twitter data. We find that our method outperforms the baseline approaches in the experiments, and that it generalizes well on different word embeddings without retraining. Our method is therefore capable of retaining most of the semantic information in the text, and is applicable out-of-the-box.Comment: 8 pages, 3 figures, 2 tables, appears in Pattern Recognition Letter

Swirlgraft versus conventional straight graft as vascular access: a full CFD-analysis

Two 3D models of an arterio-venous graft, a connection between an artery and a vein as vascular access for hemodialysis, were studied. One model of a conventional straight loop graft, the other of a graft with helical configuration (e.g. SwirlGraft (Veryan Medical, London, UK)). The statement that the helical design reduces Intimal Hyperplasia (IH) formation was studied by evaluating low wall shear stress and high oscillatory shear stress zones next to the helicity flow index. The IH-inducing zones were reduced but were not eliminated and the helicity of the flow was increased. The statement that the SwirlGraft avoids stenosis should however be considered with care in clinical practice