A new scoring system in Cystic Fibrosis: statistical tools for database analysis – a preliminary report

<p>Abstract</p> <p>Background</p> <p>Cystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21^stcentury. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system.</p> <p>Methods</p> <p>The evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets.</p> <p>Results</p> <p>(1) Feature selection: CAP has a more effective "modelling" focus than DA.</p> <p>(2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males.</p> <p>Conclusion</p> <p>Our preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset.</p

Suitability and managerial implications of a Master Surgical Scheduling approach

Abstract: Operating room (OR) planning and scheduling is a popular and challenging subject within the operational research applied to health services research (ORAHS). However, the impact in practice is very limited. The organization and culture of a hospital and the inherent characteristics of its processes impose specific implementation issues that affect the success of planning approaches. Current tactical OR planning approaches often fail to account for these issues.Master surgical scheduling (MSS) is a promising approach for hospitals to optimize resource utilization and patient flows. We discuss the pros and cons of MSS and compare MSS with centralized and decentralized planning approaches. Finally, we address various implementation issues of MSS and discuss its suitability for hospitals with different organizational foci and culture

Non-alcoholic fatty liver disease as a component of metabolic syndrome

According to current views, non-alcoholic fatty liver disease (NAFLD) is regarded as a hepatic manifestation of metabolic syndrome (MS). As an independent nosological unit NAFLD proceeds in two main forms: steatosis (steatosis of the liver), nonalcoholic steatohepatitis (NASH) with a tendency to fibrosis and subsequent cirrhosis. Excess intracellular fatty acids, oxidant stress, tumor necrosis factor-а, and mitochondrial dysfunction are causes of hepatocellular injury, thereby leading to disease progression and to the establishment of nonalcoholic steatohepatitis (NASH). In this regard, are becoming increasingly important issues of early diagnosis and treatment NAFLD in patients with metabolic syndrome.Согласно современным представлениям, неалкогольная жировая болезнь печени (НЖБП) рассматривается как печеночная манифестация метаболического синдрома (МС). Как самостоятельная нозологическая единица, НЖБП протекает в двух основных формах: жировой гепатоз (стеатоз) печени, неалкогольный стеатогепатит (НАСГ) с тенденцией развития фиброза, а в последующем цирроза печени. Превышение внутриклеточных жирных кислот, окислительный стресс, фактор некроза опухоли-а и митохондриальная дисфункция являются причинами повреждения печени, что приводит к развитию неалкогольного стеатогепатита (НАСГ). В связи с этим все большее значение приобретают вопросы ранней диагностики и лечения НЖБП у больных с метаболическим синдромом

Energy Trade and Cooperation between the EU and CIS Countries

The report reviews key issues in energy trade and cooperation between the EU and CIS countries. It describes historical trends of oil and gas demand in the EU, other European and CIS countries and offers demand forecasts until 2030. Recent developments in oil and gas production and exports from Russia and Caspian countries are covered in detail leading to the discussion of the likely export potential of these regions. The key factors determining the production outlook, trade-offs and competition related to energy resources transportation choices are also discussed. The report also covers the interests and role of transit countries in relations between producer and consumer regions. The analytical section leads to policy recommendations that focus mainly on the EU

Piecewise linear classifiers based on nonsmooth optimization approaches

Nonsmooth optimization provides efficient algorithms for solving many machine learning problems. In particular, nonsmooth optimization approaches to supervised data classification problems lead to the design of very efficient algorithms for their solution. In this chapter, we demonstrate how nonsmooth optimization algorithms can be applied to design efficient piecewise linear classifiers for supervised data classification problems. Such classifiers are developed using a max–min and a polyhedral conic separabilities as well as an incremental approach. We report results of numerical experiments and compare the piecewise linear classifiers with a number of other mainstream classifiers

Minimizing nonsmooth DC functions via successive DC piecewise-affine approximations

We introduce a proximal bundle method for the numerical minimization of a nonsmooth difference-of-convex (DC) function. Exploiting some classic ideas coming from cutting-plane approaches for the convex case, we iteratively build two separate piecewise-affine approximations of the component functions, grouping the corresponding information in two separate bundles. In the bundle of the first component, only information related to points close to the current iterate are maintained, while the second bundle only refers to a global model of the corresponding component function. We combine the two convex piecewise-affine approximations, and generate a DC piecewise-affine model, which can also be seen as the pointwise maximum of several concave piecewise-affine functions. Such a nonconvex model is locally approximated by means of an auxiliary quadratic program, whose solution is used to certify approximate criticality or to generate a descent search-direction, along with a predicted reduction, that is next explored in a line-search setting. To improve the approximation properties at points that are far from the current iterate a supplementary quadratic program is also introduced to generate an alternative more promising search-direction. We discuss the main convergence issues of the line-search based proximal bundle method, and provide computational results on a set of academic benchmark test problems. © 2017, Springer Science+Business Media, LLC

Unsupervised and supervised data classification via nonsmooth and global optimization

Clustering, classification, cluster function, nonsmooth optimization, global optimization, 65K05, 90C26, 90C30, 90C90,