Cost comparison of orthopaedic fracture pathways using discrete event simulation in a Glasgow hospital

Objective: Healthcare faces the continual challenge of improving outcome whilst aiming to reduce cost. The aim of this study was to determine the micro cost differences of the Glasgow non-operative trauma virtual pathway in comparison to a traditional pathway. Design:  Discrete event simulation was used to model and analyse cost and resource utilisation with an activity based costing approach. Data for a full comparison before the process change was unavailable so we utilised a modelling approach, comparing a Virtual Fracture Clinic (VFC) to a simulated Traditional Fracture Clinic (TFC). Setting:  The orthopaedic unit VFC pathway pioneered at Glasgow Royal Infirmary has attracted significant attention and interest and is the focus of this cost study. Outcome measures: Our study focused exclusively on non-operative trauma patients attending Emergency Department or the minor injuries unit and the subsequent step in the patient pathway. Retrospective studies of patient outcomes as a result of the protocol introductions for specific injuries in association with activity costs from the models.ResultsPatients are satisfied with the new pathway, the information provided and the outcome of their injuries (Evidence Level IV). There was a 65% reduction in the number of first outpatient face-to-face attendances in orthopaedics. In the VFC pathway, the resources required per day were significantly lower for all staff groups (p=<0.001). The overall cost per patient of the VFC pathway was £22.84 (95% CI: 21.74, 23.92) per patient compared with £36.81 (95% CI: 35.65, 37.97) for the TFC pathway.  Conclusions:  Our results give a clearer picture of the cost comparison of the virtual pathway over a wholly traditional face-to-face clinic system. The use of simulation-based stochastic costings in healthcare economic analysis has been limited to date, but this study provides evidence for adoption of this method as a basis for its application in other healthcare settings

Search for high-performance probe-fed stacked patches using optimization

High-performance circular probe-fed stacked patch antenna designs are explored through the use of numerical optimization. New trends are sought to aid understanding and to suggest novel solutions. We describe the optimization technique, present a new design trend relating efficiency and bandwidth to the choice of substrate dielectric, and propose and demonstrate a novel, optimized antenna achieving 33% bandwidth whilst maintaining greater than 80% surface wave efficiency

Level Eulerian Posets

The notion of level posets is introduced. This class of infinite posets has the property that between every two adjacent ranks the same bipartite graph occurs. When the adjacency matrix is indecomposable, we determine the length of the longest interval one needs to check to verify Eulerianness. Furthermore, we show that every level Eulerian poset associated to an indecomposable matrix has even order. A condition for verifying shellability is introduced and is automated using the algebra of walks. Applying the Skolem--Mahler--Lech theorem, the ${\bf ab}$-series of a level poset is shown to be a rational generating function in the non-commutative variables ${\bf a}$ and ${\bf b}$. In the case the poset is also Eulerian, the analogous result holds for the ${\bf cd}$-series. Using coalgebraic techniques a method is developed to recognize the ${\bf cd}$-series matrix of a level Eulerian poset

Learning Acoustic Word Embeddings With Dynamic Time Warping Triplet Networks

In the last years, acoustic word embeddings (AWEs) have gained significant interest in the research community. It applies specifically to the application of acoustic embeddings in the Query-by Example Spoken Term Detection (QbE-STD) search and related word discrimination tasks. It has been shown that AWEs learned for the word or phone classification in one or several languages can outperform approaches that use dynamic time warping (DTW). In this paper, a new method of learning AWEs in the DTW framework is proposed. It employs a multitask triplet neural network to generate the AWEs. The triplet network learns acoustic representations of words through a comparison of DTW distances. In addition, a multitask objective, including a conventional word classification component, and a triplet loss component is proposed. The triplet loss component applies the DTW distance for the word discrimination task. The multitask objective ensures that the embeddings can be used with DTW directly. Experimental validation shows that the proposed approach is well-suited, but not necessarily restricted to the QbE-STD search. A comparison with several baseline methods shows that the new method leads to a significant improvement of the results on the word discrimination task. An evaluation of the word clustering in the learned embedding space is presented

Adversarial Learning Approach to Unsupervised Labeling of Fine Art Paintings

An automatic classification of fine art images is limited by the scarcity of high-quality labels made by art experts. This study aims to provide meaningful automatic labeling of fine art paintings (machine labeling) without the need for human annotation. A new unsupervised Adversarial Clustering System (ACS) is proposed. The ACS is an adversarial learning approach comprising an unsupervised clustering module generating machine labels and a supervised classification module classifying the data based on the machine labels. Both modules are linked through an optimization algorithm iteratively improving the unsupervised clusters. The objective function driving the improvement consists of the within-cluster sum of squares (WCSS) error and the supervised classification accuracy. The proposed method was tested on three different fine-art datasets, including two sets of paintings previously categorized by art experts and one never categorized collection of Australian Aboriginal paintings. The unsupervised clusters were analyzed using standard unsupervised clustering metrics and a reliability measure between machine and human labeling. The ACS showed higher reliability compared to the classical k-means clustering method. The content analysis of unsupervised clusters indicated grouping based on scene composition, type, and shape of the object, edge sharpness and direction, and color palette

A Complete Key Management Scheme for LoRaWAN v1.1

Security is one of the major concerns of the Internet of Things (IoT) wireless technologies. LoRaWAN is one of the emerging Low Power Wide Area Networks being developed for IoT applications. The latest LoRaWAN release v.1.1 has provided a security framework that includes data confidentiality protection, data integrity check, device authentication and key management. However, its key management part is only ambiguously defined. In this paper, a complete key management scheme is proposed for LoRaWAN. The scheme addresses key updating, key generation, key backup, and key backward compatibility. The proposed scheme was shown not only to enhance the current LoRaWAN standard, but also to meet the primary design consideration of LoRaWAN, i.e., low power consumption

Simultaneous Sleep Stage and Sleep Disorder Detection from Multimodal Sensors Using Deep Learning

Sleep scoring involves the inspection of multimodal recordings of sleep data to detect potential sleep disorders. Given that symptoms of sleep disorders may be correlated with specific sleep stages, the diagnosis is typically supported by the simultaneous identification of a sleep stage and a sleep disorder. This paper investigates the automatic recognition of sleep stages and disorders from multimodal sensory data (EEG, ECG, and EMG). We propose a new distributed multimodal and multilabel decision-making system (MML-DMS). It comprises several interconnected classifier modules, including deep convolutional neural networks (CNNs) and shallow perceptron neural networks (NNs). Each module works with a different data modality and data label. The flow of information between the MML-DMS modules provides the final identification of the sleep stage and sleep disorder. We show that the fused multilabel and multimodal method improves the diagnostic performance compared to single-label and single-modality approaches. We tested the proposed MML-DMS on the PhysioNet CAP Sleep Database, with VGG16 CNN structures, achieving an average classification accuracy of 94.34% and F1 score of 0.92 for sleep stage detection (six stages) and an average classification accuracy of 99.09% and F1 score of 0.99 for sleep disorder detection (eight disorders). A comparison with related studies indicates that the proposed approach significantly improves upon the existing state-of-the-art approaches

Distributed Neural Network System for Multimodal Sleep Stage Detection

Existing automatic sleep stage detection methods predominantly use convolutional neural network classifiers (CNNs) trained on features extracted from single-modality signals such as electroencephalograms (EEG). On the other hand, multimodal approaches propose very complexly stacked network structures with multiple CNN branches merged by a fully connected layer. It leads to very high computational and data requirements. This study proposes replacing a stacked network with a distributed neural network system for multimodal sleep stage detection. It has relatively low computational and training data requirements while providing highly competitive results. The proposed multimodal classification and decision-making system (MM-DMS) method applies a fully connected shallow neural network, arbitrating between classification outcomes given by an assembly of independent convolutional neural networks (CNNs), each using a different single-modality signal. Experiments conducted on the CAP Sleep Database data, including the EEG-, ECG-, and EMG modalities representing six stages of sleep, show that the MM-DMS significantly outperforms each single-modality CNN. The fully-connected shallow network arbitration included in the MM-DMS outperforms the traditional majority voting-, average probability-, and maximum probability decision-making methods