A genetic algorithm based on nearest neighbour classification to breast cancer diagnosis

Copyright © 2003 ACPSEM. All rights reserved. The document attached has been archived with permission from the publisher.R. Jain and J. Mazumda

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

A Survey of Insulin-Dependent Diabetes—Part I: Therapies and Devices

This paper surveys diabetes therapies from telemedicine viewpoint. In type 1 diabetes therapies, the exogenous insulin replacement is generally considered as a primary treatment. However, the complete replacement of exogenous insulin is still a challenging issue because of its complexity of modeling the dynamics, which is typically modeled nonlinearly. On the other hand, thanks to the progress of medical devices, currently the diabetes therapies are being automated. These medical devices include automated insulin pumps and blood glucose sensors. Insulin pumps are designed to create artificial insulin perfusion while they largely rely on the blood glucose profile measurements and these measurements are achieved by one or more blood glucose sensors. The blood glucose measurements are also important for the insulin-dependent diabetes therapies. An insulin pump along with sensors establishes a good feedback system providing the appropriate amount of the exogenous insulin on demand. Controlling the amount of exogenous insulin to suppress the blood glucose levels requires complicated computations. This paper mostly explains both type 1 and 2 diabetes and their mechanisms accompanied by descriptions of diabetes therapy and medical devices currently utilized in the therapy

VLSI Design

This book provides some recent advances in design nanometer VLSI chips. The selected topics try to present some open problems and challenges with important topics ranging from design tools, new post-silicon devices, GPU-based parallel computing, emerging 3D integration, and antenna design. The book consists of two parts, with chapters such as: VLSI design for multi-sensor smart systems on a chip, Three-dimensional integrated circuits design for thousand-core processors, Parallel symbolic analysis of large analog circuits on GPU platforms, Algorithms for CAD tools VLSI design, A multilevel memetic algorithm for large SAT-encoded problems, etc

Heath-PRIOR: An Intelligent Ensemble Architecture to Identify Risk Cases in Healthcare

Smart city environments, when applied to healthcare, improve the quality of people\u27s lives, enabling, for instance, disease prediction and treatment monitoring. In medical settings, case prioritization is of great importance, with beneficial outcomes both in terms of patient health and physicians\u27 daily work. Recommender systems are an alternative to automatically integrate the data generated in such environments with predictive models and recommend actions, content, or services. The data produced by smart devices are accurate and reliable for predictive and decision-making contexts. This study main purpose is to assist patients and doctors in the early detection of disease or prediction of postoperative worsening through constant monitoring. To achieve this objective, this study proposes an architecture for recommender systems applied to healthcare, which can prioritize emergency cases. The architecture brings an ensemble approach for prediction, which adopts multiple Machine Learning algorithms. The methodology used to carry out the study followed three steps. First, a systematic literature mapping, second, the construction and development of the architecture, and third, the evaluation through two case studies. The results demonstrated the feasibility of the proposal. The predictions are promising and adherent to the application context for accurate datasets with a low amount of noises or missing values

Optimierung der Hämodialyse durch den Einsatz von Fuzzy-Logik zur Vermeidung symptomatischer Blutdruckabfälle

Der Einsatz einer neuen Generation von Regelsystemen, die eine automatische Therapieführung gestatten, trägt zur Reduktion dialyseassozierter Komplikationen an der Hämodialyse bei. Der Einsatz einer Fuzzy-Controller gesteuerten, closed loop - Regelung führte zu einer signifikanten Stabilisierung des Blutdruckes. Hypotoniegefährdete Patienten wurden in über 1000 einzelnen Hämodialysen nach verschiedenen Vorgehensweisen behandelt: durch Fuzzy-Controller geregelte Infusion, durch Fuzzy-Controller Regelung der Ultrafiltration und der Dialysatleitfähigkeit bzw. die Kombination der beiden letzteren.The introduction of a new generation of control systems contributes to a reduction of complications during hemodialysis. A fuzzy-controlled closed-loop system resulted in a significant stabilization of blood pressure. Patients prone to hypotension during hemodialysis were treated in over 1000 single sessions with different procedures: Fuzzy Controller regulated infusion, Fuzzy Controller regulated ultrafiltration rate, Fuzzy Controller regulated dialysate conductivity and simultaneous regulation of ultrafiltration and conductivity

Detection and Classification of Diabetic Retinopathy Pathologies in Fundus Images

Diabetic Retinopathy (DR) is a disease that affects up to 80% of diabetics around the world. It is the second greatest cause of blindness in the Western world, and one of the leading causes of blindness in the U.S. Many studies have demonstrated that early treatment can reduce the number of sight-threatening DR cases, mitigating the medical and economic impact of the disease. Accurate, early detection of eye disease is important because of its potential to reduce rates of blindness worldwide. Retinal photography for DR has been promoted for decades for its utility in both disease screening and clinical research studies. In recent years, several research centers have presented systems to detect pathology in retinal images. However, these approaches apply specialized algorithms to detect specific types of lesion in the retina. In order to detect multiple lesions, these systems generally implement multiple algorithms. Furthermore, some of these studies evaluate their algorithms on a single dataset, thus avoiding potential problems associated with the differences in fundus imaging devices, such as camera resolution. These methodologies primarily employ bottom-up approaches, in which the accurate segmentation of all the lesions in the retina is the basis for correct determination. A disadvantage of bottom-up approaches is that they rely on the accurate segmentation of all lesions in order to measure performance. On the other hand, top-down approaches do not depend on the segmentation of specific lesions. Thus, top-down methods can potentially detect abnormalities not explicitly used in their training phase. A disadvantage of these methods is that they cannot identify specific pathologies and require large datasets to build their training models. In this dissertation, I merged the advantages of the top-down and bottom-up approaches to detect DR with high accuracy. First, I developed an algorithm based on a top-down approach to detect abnormalities in the retina due to DR. By doing so, I was able to evaluate DR pathologies other than microaneurysms and exudates, which are the main focus of most current approaches. In addition, I demonstrated good generalization capacity of this algorithm by applying it to other eye diseases, such as age-related macular degeneration. Due to the fact that high accuracy is required for sight-threatening conditions, I developed two bottom-up approaches, since it has been proven that bottom-up approaches produce more accurate results than top-down approaches for particular structures. Consequently, I developed an algorithm to detect exudates in the macula. The presence of this pathology is considered to be a surrogate for clinical significant macular edema (CSME), a sight-threatening condition of DR. The analysis of the optic disc is usually not taken into account in DR screening systems. However, there is a pathology called neovascularization that is present in advanced stages of DR, making its detection of crucial clinical importance. In order to address this problem, I developed an algorithm to detect neovascularization in the optic disc. These algorithms are based on amplitude-modulation and frequency-modulation (AM-FM) representations, morphological image processing methods, and classification algorithms. The methods were tested on a diverse set of large databases and are considered to be the state-of the art in this field