Perspectives of Patients with Insulin-Treated Type 1 and Type 2 Diabetes on Hypoglycemia: Results of the HAT Observational Study in Central and Eastern European Countries

INTRODUCTION: The aim of this study was to determine the level of awareness of hypoglycemia, the level of fear for hypoglycemia, and the response to hypoglycemic events among insulin-treated diabetes patients from Central and Eastern Europe (CEE). The impact of hypoglycemia on the use of healthcare resources and patient productivity was also assessed. METHODS: This was a multicenter, non-interventional, two-part, patient self-reported questionnaire study that comprised both a retrospective cross-sectional evaluation and a prospective observational evaluation. Study participants were insulin-treated adult patients with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) from CEE. RESULTS: Most patients (85.4% T1DM and 83.6% T2DM) reported normal hypoglycemia awareness. The median hypoglycemia fear score was 5 out of 10 for T1DM and 4 out of 10 for T2DM patients. Patients increased glucose monitoring, consulted a doctor/nurse, and/or reduced the insulin dose in response to hypoglycemia. As a consequence of hypoglycemia, patients took leave from work/studies or arrived late and/or left early. Hospitalization was required for 31 (1.2%) patients with T1DM and 66 (2.1%) patients with T2DM. CONCLUSION: Hypoglycemia impacts patients' personal and social functioning, reduces productivity, and results in additional costs, both direct (related to increased use of healthcare resources) and indirect (related to absenteeism. FUNDING: Novo Nordisk

Partial volume effect detection in MRI segmentation based on approximate decision reducts

Segmentation of Magnetic Resonance Imaging (MRI) is a process of assigning tissue class labels to voxels. One of the main sources of segmentation error is the partial volume effect (PVE) which occurs most often with low resolution images - with large voxels, the probability of a voxel containing multiple tissue classes increases. We propose a multistage algorithm for segmenting MRI images with a mid-stage of recognizing the PVE voxels. The information about PVE regions added to other voxels features extracted from the image can increase the overall accuracy of the segmentation. In our methods we have utilize a classification approach based on approximate decision reducts derived from the data mining paradigm of the theory of rough sets. An approximate reduct is an irreducible subset of features, which enables to classify decision concepts with a satisfactory degree of accuracy in the training data. The ensembles of best found reducts trained for appropriate approximation degrees are applied to detection of the PVE and performing the segmentation

A hybrid approach to MR imaging segmentation using unsupervised clustering and approximate reducts

We introduce a hybrid approach to magnetic resonance image segmentation using unsupervised clustering and the rules derived from approximate decision reducts. We utilize the MRI phantoms from the Simulated Brain Database. We run experiments on randomly selected slices from a volumetric set of multi-modal MR images (T1, T2, PD). Segmentation accuracy reaches 96% for the highest resolution images and 89% for the noisiest image volume. We also tested the resultant classifier on real clinical data, which yielded an accuracy of approximately 84%

A rough set-based magnetic resonance imaging partial volume detection system

Segmentation of magnetic resonance imaging (MRI) data entails assigning tissue class labels to voxels. The primary source of segmentation error is the partial volume effect (PVE) which occurs most often with low resolution imaging - With large voxels, the probability of a voxel containing multiple tissue classes increases. Although the PVE problem has not been solved, the first stage entails correctly identifying PVE voxels. We employ rough sets to identify them automatically

Semi-Supervised Fuzzy-Rough Feature Selection

With the continued and relentless growth in dataset sizes in recent times, feature or attribute selection has become a necessary step in tackling the resultant intractability. Indeed, as the number of dimensions increases, the number of corresponding data instances required in order to generate accurate models increases exponentially. Fuzzy-rough set-based feature selection techniques offer great flexibility when dealing with real-valued and noisy data; however, most of the current approaches focus on the supervised domain where the data object labels are known. Very little work has been carried out using fuzzy-rough sets in the areas of unsupervised or semi-supervised learning. This paper proposes a novel approach for semi-supervised fuzzy-rough feature selection where the object labels in the data may only be partially present. The approach also has the appealing property that any generated subsets are also valid (super)reducts when the whole dataset is labelled. The experimental evaluation demonstrates that the proposed approach can generate stable and valid subsets even when up to 90% of the data object labels are missing