What impact did a Paediatric Early Warning system have on emergency admissions to the paediatric intensive care unit? An observational cohort study

Summary The ideology underpinning Paediatric Early Warning systems (PEWs) is that earlier recognition of deteriorating in-patients would improve clinical outcomes. Objective To explore how the introduction of PEWs at a tertiary children's hospital affects emergency admissions to the Paediatric Intensive Care Unit (PICU) and the impact on service delivery. To compare ‘in-house’ emergency admissions to PICU with ‘external’ admissions transferred from District General Hospitals (without PEWs). Method A before-and-after observational study August 2005–July 2006 (pre), August 2006–July 2007 (post) implementation of PEWs at the tertiary children's hospital. Results The median Paediatric Index of Mortality (PIM2) reduced; 0.44 vs 0.60 (p < 0.001). Fewer admissions required invasive ventilation 62.7% vs 75.2% (p = 0.015) for a shorter median duration; four to two days. The median length of PICU stay reduced; five to three days (p = 0.002). There was a non-significant reduction in mortality (p = 0.47). There was no comparable improvement in outcome seen in external emergency admissions to PICU. A 39% reduction in emergency admission total beds days reduced cancellation of major elective surgical cases and refusal of external PICU referrals. Conclusions Following introduction of PEWs at a tertiary children's hospital PIM2 was reduced, patients required less PICU interventions and had a shorter length of stay. PICU service delivery improved

Numerical approach at non-abelian gauge theory

SIGLELD:D47766/83 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Neural network identification and control of an underwater vehicle

Real-time predictive control requires a forward model that is both accurate and fast. This paper introduces two nonlinear internal memory network architectures and compares their performance with a Multi-layer Perceptron (MLP) augmented with the use of spread encoding. The test plant is a single component from an Underwater Robotic Vehicle (URV), comprising a thruster encased in a steel frame and provided with buoyancy. This assemblv is free to move under water and is controlled for depth. The internal memory networks are of comparable accuracy to the MLP but more parsimonious, resulting, in a faster response which makes them better suited for on-line control. Although a particular case study is presented as the focus of this paper, the algorithms and methods developed have generic applicability

Neural Network Modelling and Control for Underwater Vehicles

Neural networks are currently finding practical applications ranging from ‘soft’ regulatory control in consumer products to accurate control of non-linear plant in the process industries. This paper describes the application of neural networks to modelling and control of a prototype underwater vehicle, as an example of a system containing severe non-linearities. The most common implementation strategy for neural control is model predictive control, where a model of the process is developed first and is used off-line to design an appropriate compensator. The accuracy and robustness of this control strategy relies on the quality of the non-linear process model, in particular its ability to predict the plant response accurately multiple-steps ahead. In this paper, several neural network architectures are used to evaluate a long-range model predictive control structure, both in simulation and for on-line control of vehicle depth, achieving accurate control with a smooth actuator signal

Neural Network Based Predictive Control Systems for Underwater Robotic Vehicles

Oceanographic exploration is one of the fast emerging applications of robotics, and the design of controllers for Underwater Robotic Vehicles (URVs) is as challenging as for land based ones. The difficulties in modelling an URV and its hazardous environment restrict the use of conventional controllers. This paper presents an approach for control and system identification of a prototype URV, as an example of a system containing severe non-linearities, using neural networks (NNs). NNs models are developed and then incorporated into a predictive control strategy which are evaluated on-line. Results are shown for both the modelling and control of the system including hybrid control strategies which combine neural predictive with conventional three term controllers

Machine learning in cancer research : implications for personalised medicine

Driven by the growing demand of personalization of medical procedures, data-based, computer-aided cancer research in human patients is advancing at an accelerating pace, providing a broadening landscape of opportunity for Machine Learning methods. This landscape can be observed from the wide-reaching view of population studies down to the genotype detail. In this brief paper, we provide a sweeping glimpse, by no means exhaustive, of the state-of-the-art in this field at the different scales of data measurement and analysis

Probabilistic Modeling in Machine Learning

Probabilistic methods are the heart of machine learning. This chapter shows links between core principles of information theory and probabilistic methods, with a short overview of historical and current examples of unsupervised and inferential models. Probabilistic models are introduced as a powerful idiom to describe the world, using random variables as building blocks held together by probabilistic relationships. The chapter discusses how such probabilistic interactions can be mapped to directed and undirected graph structures, which are the Bayesian and Markov networks. We show how these networks are subsumed by the broader class of the probabilistic graphical models, a general framework that provides concepts and methodological tools to encode, manipulate and process probabilistic knowledge in a computationally efficient way. The chapter then introduces, in more detail, two topical methodologies that are central to probabilistic modeling in machine learning. First, it discusses latent variable models, a probabilistic approach to capture complex relationships between a large number of observable and measurable events (data, in general), under the assumption that these are generated by an unknown, nonobservable process. We show how the parameters of a probabilistic model involving such nonobservable information can be efficiently estimated using the concepts underlying the expectation–maximization algorithms. Second, the chapter introduces a notable example of latent variable model, that is of particular relevance for representing the time evolution of sequence data, that is the hidden Markov model . The chapter ends with a discussion on advanced approaches for modeling complex data-generating processes comprising nontrivial probabilistic interactions between latent variables and observed information