Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

Heart failure disease management program experience in 4,545 heart failure admissions to a community hospital

Background Disease management programs (DMPs) are developed to address the high morbi-mortality and costs of congestive heart failure (CHF). Most studies have focused on intensive programs in academic centers. Washington County Hospital (WCH) in Hagerstown, MD, the primary reference to a semirural county, established a CHF DMP in 2001 with standardized documentation of screening and participation. Linkage to electronic records and state vital statistics enabled examination of the CHF population including individuals participating and those ineligible for the program. Methods All WCH inpatients with CHF International Classification of Diseases, Ninth Revision code in any position of the hospital list discharged alive. Results Of 4,545 consecutive CHF admissions, only 10% enrolled and of those only 52.2% made a call. Enrollment in the program was related to: age (OR 0.64 per decade older, 95% CI 0.58-0.70), CHF as the main reason for admission (OR 3.58, 95% CI 2.4-4.8), previous admission for CHF (OR 1.14, 95% CI 1.09-1.2), and shorter hospital stay (OR 0.94 per day longer, 95% CI 0.87-0.99). Among DMP participants mortality rates were lowest in the first month (80/1000 person-years) and increased subsequently. The opposite mortality trend occurred in nonenrolled groups with mortality in the first month of 814 per 1000 person-years in refusers and even higher in ineligible (1569/1000 person-years). This difference remained significant after adjustment. Re-admission rates were lower among participants who called consistently (adjusted incidence rate ratio 0.62, 95% CI 0.52-0.77). Conclusion Only a small and highly select group participated in a low-intensity DMP for CHF in a community-based hospital. Design of DMPs should incorporate these strong selective factors to maximize program impact. (Am Heart J 2009; 15 8:459-66.)

Clinical Evaluation of the NS1 Antigen-Capture ELISA for Early Diagnosis of Dengue Virus Infection in Brazil

The fact that the diagnosis of infection with dengue virus is usually made by detecting IgM antibodies during the convalescent phase of the disease interferes with disease management and, consequently, with reducing mortality rates. This study evaluated the sensitivity and specificity of detection of NS1 in samples of patients suspected of acute dengue virus infection in Brazil. The results were used to institute treatment and the sensitivity and specificity of detection of NS1 were compared to the results of detection of IgM, virus isolation, and RT-PCR. Detection of NS1 yielded better results than RTPCR and virus isolation. When considering IgM detection and RT-PCR positive results as ""gold standards,"" the sensitivity and specificity of the NS1 assay were 95.9% and 81.1%, respectively. All patients enrolled in the study were treated promptly and had an uneventful course of the disease. The detection of NS1 provided better results than the diagnostic techniques used currently during the acute phase of disease (RT-PCR and virus isolation). Detection of NS1 is an important tool for the diagnosis of acute dengue infection, particularly in highly endemic areas, allowing for rapid treatment of patients and reduction of disease burden. J. Med. Virol. 82: 1400-1405, 2010. (C) 2010 Wiley-Liss, Inc.

Student characteristics and academic success: An econometric study of the performance of "equity groups"

Like previous volumes in the Educational Innovation in Economics and Business Series, this book is genuinely international in terms of its coverage. With contributions from nine different countries and three continents, it reflects a global interest in, and commitment to, innovation in business education, with a view to enhancing the learning experience of both undergraduates and postgraduates. It should prove of value to anyone engaged directly in business education, defined broadly to embrace management, finance, marketing, economics, informational studies, and ethics, or who has responsibility for fostering the professional development of business educators. The contributions have been selected with the objective of encouraging and inspiring others as well as illustrating developments in the sphere of business education. This volume brings together a collection of articles describing different aspects of the developments taking place in today’s workplace and how they affect business education. It describes strategies for breaking boundaries for global learning. These target specific techniques regarding teams and collaborative learning, transitions from academic settings to the workplace, the role of IT in the learning process, and program-level innovation strategies. This volume addresses issues faced by professionals in higher and further education and also those involved in corporate training centers and industry.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.