Abstract

Abstract
Implantable electronic cardiovascular devices (IECD) include a broad spectrum of devices that have the ability to maintain rhythm, provide cardiac resynchronization therapy, and/or prevent sudden cardiac death. The incidence of bradyarrhythmias and other cardiac problems led to a broader use of IECD, which turned traditional follow-up into an extremely heavy burden for healthcare systems to support. Our aim was to assess the impact of remote monitoring on the follow-up of patients with IECD. We performed a review through PubMed using a specific query. The paper selection process included a three-step approach in which title, abstract, and cross-references were analyzed. Studies were then selected using previously defined inclusion criteria and analyzed according to the country of origin of the study, year, and journal of publication; type of study; and main issues covered. Twenty articles were included in this review. Eighty percent of the selected papers addressed clinical issues, from which 94% referred clinical events identification, clinical stability, time savings, or physician satisfaction as advantages, whereas 38% referred disadvantages that included both legal and technical issues. Forty-five percent of the papers referred patient issues, from which 89% presented advantages, focusing on patient acceptance/satisfaction, and patient time-savings. The main downsides were technical issues but patient privacy was also addressed. All the papers dealing with economic issues (20%) referred both advantages and disadvantages equally. Remote monitoring is presently a safe technology, widely accepted by patients and physicians, for its convenience, reassurance, and diagnostic potential. This review summarizes the principles of remote IECD monitoring presenting the current state-of-the-art. Patient safety and device interaction, applicability of current technology, and limitations of remote IECD monitoring are also addressed. The use of remote monitor should consider the selection of patients, the type of disease, and centers' availability to receive, interpret and respond to device alerts. Before remote IECD monitoring can be routinely used, technical, procedure, and ethical/legal issues should be addressed.

Abstract

Abstract
Currently many classification algorithms exist and no algorithm exists that would outperform all the others. Therefore it is of interest to determine which classification algorithm is the best one for a given task. Although direct comparisons can be made for any given problem using a cross-validation evaluation, it is desirable to avoid this, as the computational costs are significant. We describe a method which relies on relatively fast pairwise comparisons involving two algorithms. This method is based on a previous work and exploits sampling landmarks, that is information about learning curves besides classical data characteristics. One key feature of this method is an iterative procedure for extending the series of experiments used to gather new information in the form of sampling landmarks. Metalearning plays also a vital role. The comparisons between various pairs of algorithm are repeated and the result is represented in the form of a partially ordered ranking. Evaluation is done by comparing the partial order of algorithm that has been predicted to the partial order representing the supposedly correct result. The results of our analysis show that the method has good performance and could be of help in practical applications. © 2010 Springer-Verlag Berlin Heidelberg.

Abstract
Currently many classification algorithms exist and there is no algorithm that would outperform all the others in all tasks. Therefore it is of interest to determine which classification algorithm is the best one for a given task. Although direct comparisons can be made for any given problem using a cross-validation evaluation, it is desirable to avoid this, as the computational costs are significant. We describe a method which relies on relatively fast pairwise comparisons involving two algorithms. This method exploits sampling landmarks, that is information about learning curves besides classical data characteristics. One key feature of this method is an iterative procedure for extending the series of experiments used to gather new information in the form of sampling landmarks. Metalearning plays also a vital role. The comparisons between various pairs of algorithm are repeated and the result is represented in the form of a partially ordered ranking. Evaluation is done by comparing the partial order of algorithm that has been predicted to the partial order representing the supposedly correct result. The results of our analysis show that the method has good performance and could be of help in practical applications.

Abstract
We describe a new unsupervised approach for synonymy discovery by aligning paraphrases in monolingual domain corpora. For that purpose, we identify phrasal terms that convey most of the concepts within domains and adapt a methodology for the automatic extraction and alignment of paraphrases to identify paraphrase casts from which valid synonyms are discovered. Results performed on two different domain corpora show that general synonyms as well as synonymic expressions can be identified with a 67.27% precision.