Treatment of acute diverticulitis laparoscopic lavage vs. resection (DILALA): study protocol for a randomised controlled trial

<p>Abstract</p> <p>Background</p> <p>Perforated diverticulitis is a condition associated with substantial morbidity. Recently published reports suggest that laparoscopic lavage has fewer complications and shorter hospital stay. So far no randomised study has published any results.</p> <p>Methods</p> <p>DILALA is a Scandinavian, randomised trial, comparing laparoscopic lavage (LL) to the traditional Hartmann's Procedure (HP). Primary endpoint is the number of re-operations within 12 months. Secondary endpoints consist of mortality, quality of life (QoL), re-admission, health economy assessment and permanent stoma. Patients are included when surgery is required. A laparoscopy is performed and if Hinchey grade III is diagnosed the patient is included and randomised 1:1, to either LL or HP. Patients undergoing LL receive > 3L of saline intraperitoneally, placement of pelvic drain and continued antibiotics. Follow-up is scheduled 6-12 weeks, 6 months and 12 months. A QoL-form is filled out on discharge, 6- and 12 months. Inclusion is set to 80 patients (40+40).</p> <p>Discussion</p> <p>HP is associated with a high rate of complication. Not only does the primary operation entail complications, but also subsequent surgery is associated with a high morbidity. Thus the combined risk of treatment for the patient is high. The aim of the DILALA trial is to evaluate if laparoscopic lavage is a safe, minimally invasive method for patients with perforated diverticulitis Hinchey grade III, resulting in fewer re-operations, decreased morbidity, mortality, costs and increased quality of life.</p> <p>Trial registration</p> <p>British registry (ISRCTN) for clinical trials <a href="http://www.controlled-trials.com/ISRCTN82208287">ISRCTN82208287</a><url>http://www.controlled-trials.com/ISRCTN82208287</url></p

Exchange Rate Prediction using Support Vector Machines: A comparison with Artificial Neural Networks

Financial forecasting in general, and exchange rate prediction in particular, is an issue of much interest to both academic and economic communities. Being able to accurately forecast exchange rate movements provides considerable benefits to both firms and investors. This research aims to propose a decision support aid to these firms and investors, enabling them to better anticipate on possible future exchange rate movements, based on one of the most promising prediction models recently developed within computational intelligence, the Support Vector Machine. The economics of supply and demand largely determine the exchange rate fluctuations. Calculating the supply and demand curves to determine the exchange rate has shown to be unfeasible. Therefore, one needs to rely on various forecasting methods. The traditional linear forecasting methods suffer from their linear nature, since empirical evidence has demonstrated the existence of nonlinearities in exchange rates. In addition, the usefulness of the parametric, and nonparametric, nonlinear models, has shown to be restricted. For these reasons, the use of computational intelligence in predicting the Euro Dollar exchange rate (EUR/USD) is investigated, in which these previously mentioned limitations may be overcome. The methods used are the Artificial Neural Network (ANN) and the Support Vector Machine (SVM). The ANN, more specifically the Multilayer Perceptron, is composed of several layers containing nodes that are interconnected, allowing the neurons to signal each other as information is processed. The basic idea of the SVM is finding a maximum margin classifier that separates a training set between positive and negative classes, based on a discriminant function that maximizes the geometric margin. The model selection for the prediction models was chosen to be based on the bias-variance dilemma, which denotes the trade-off between the amount of variation within different estimators on different values of a specific data set (variation) and the difference between the estimator’s expected value and the true value of the parameter being estimated (bias). Experiments on the Mackey-Glass dataset and on the EUR/USD dataset have yielded some appropriate parameter ranges for the ANN and SVM. On theoretical grounds, it has been shown that SVMs have a few interesting properties which may support the notion that SVMs generally perform better than ANNs. However, on empirical grounds, based on experimentation results in this research, no solid conclusion could be drawn regarding which model performed the best on the EUR/USD data set. Nevertheless, in light of providing firms and investors the necessary knowledge to act accordingly on possible future exchange rate movements, the SVM prediction model may still be used as a decision-support aid for this particular purpose. While the predictions on their own as provided by the SVM are not necessarily accurate, they may provide some added value in combination with other models. In addition, users of the model may learn to interpret the predictions in such a way, that they still signal some sort of relevant information.Management of TechnologySection of Information and Communication TechnologyTechnology, Policy and Managemen

Het ontwerp van intelligente software voor energiebezuiniging met behulp van patroonherkenning in het elektriciteitsverbruik

This thesis presents software that controls electrical household appliances. It uses an intelligent algorithm that adapts to the use of these household appliances by recognizing patterns of electricity useage.Electrical Engineering, Mathematics and Computer Scienc