Governing drug reimbursement policy in Poland: The role of the state, civil society, and the private sector

This article investigates the distribution of power in Poland’s drug reimbursement policy in the early 2000s. We examine competing theoretical expectations suggested by neopluralism, historical institutionalism, corporate domination, and clique theory of the post-communist state, using data from a purposive sample of 109 semi-structured interviews and documentary sources. We have four concrete findings. First, we uncovered rapid growth in budgetary spending on expensive drugs for narrow groups of patients. Second, to achieve these favorable policy outcomes drug companies employed two prevalent methods of lobbying: informal persuasion of key members of local cliques and endorsements expressed by patient organizations acting as seemingly independent “third parties.” Third, medical experts were co-opted by multinational drug companies because they relied on these firms for scientific and financial resources that were crucial for their professional success. Finally, there was one-way social mobility from the state to the pharmaceutical sector, not the “revolving door” pattern familiar from advanced capitalist countries, with deleterious consequences for state capacity. Overall, the data best supported a combination of corporate domination and clique theory: drug reimbursement in Poland was dominated by Western multinationals in collaboration with domestically based cliques.Piotr Ozieranski is indebted to the Department of Sociology, University of Cambridge and St Edmund’s College for research grants

Diagnosing skin melanoma: current versus future directions

A new database containing 410 cases of nevi pigmentosi, in four categories: benign nevus, blue nevus, suspicious nevus and melanoma malignant, carefully verified by histopathology, is described. The database is entirely different from the base presented previously, and can be readily used for research based on the so-called constructive induction in machine learning. To achieve this, the database features a different set of thirteen descriptive attributes, with a fourteenth additional attribute computed by applying values of the remaining thirteen attributes. In addition, a new program environment for the validation of computer-assisted diagnosis of melanoma, is briefly discussed. Finally, results are presented on determining optimal coefficients for the well-known ABCD formula, useful for melanoma diagnosis

On the Evolution of Rough Set Exploration System

We present the next version (ver. 2.1) of the Rough Set Exploration System -- a software tool featuring a library of methods and a graphical user interface supporting variety of rough-set-based and related computations. Methods, features and abilities of the implemented software are discussed and illustrated with examples in data analysis and decision support

Improving css-KNN classification performance by shifts in training data

This paper presents a new approach to improve the performance of a css-k-NN classifier for categorization of text documents. The css-k-NN classifier (i.e., a threshold-based variation of a standard k-NN classifier we proposed in [1]) is a lazy-learning instance-based classifier. It does not have parameters associated with features and/or classes of objects, that would be optimized during off-line learning. In this paper we propose a training data preprocessing phase that tries to alleviate the lack of learning. The idea is to compute training data modifications, such that class representative instances are optimized before the actual k-NN algorithm is employed. The empirical text classification experiments using mid-size Wikipedia data sets show that carefully crossvalidated settings of such preprocessing yields significant improvements in k-NN performance compared to classification without this step. The proposed approach can be useful for improving the effectivenes of other classifiers as well as it can find applications in domain of recommendation systems and keyword-based searc

The rough set exploration system

This article gives an overview of the Rough Set Exploration System (RSES). RSES is a freely available software system toolset for data exploration, classification support and knowledge discovery. The main functionalities of this software system are presented along with a brief explanation of the algorithmic methods used by RSES. Many of the RSES methods have originated from rough set theory introduced by Zdzislaw Pawlak during the early 1980s

Discovery of Decision Rules by Matching New Objects Against Data Tables

. In this paper we present an exemplary algorithm classifying new objects by matching them directly against data table to generate relevant decision instead of matching it against all rules generated from data table (see [1]). We report results of experiments on three medical data sets, concerning lymphography, breast cancer and primary tumor (see [8]). We compare standard methods for extracting laws from decision tables (see e.g. [17], [1]), based on rough set (see [13]) and boolean reasoning (see [2]), with the method based on algorithms calculating relevant decision rules for new objects. We also compare the results of computer experiments on those data sets obtained by applying our system based on rough set methods with the results on the same data sets obtained with help of several data analysis systems known from literature. 1 Introduction A classification algorithm is an algorithm which permits us to repeatedly make a forecast on the basis of accumulated knowledge i..