BCAS: A Web-enabled and GIS-based Decision Support System for the Diagnosis and Treatment of Breast Cancer

For decades, geographical variations in cancer rates have been observed but the precise determinants of such geographic differences in breast cancer development are unclear. Various statistical models have been proposed. Applications of these models, however, require that the data be assembled from a variety of sources, converted into the statistical models’ parameters and delivered effectively to researchers and policy makers. A web-enabled and GIS-based system can be developed to provide the needed functionality. This article overviews the conceptual web-enabled and GIS-based system (BCAS), illustrates the system’s use in diagnosing and treating breast cancer and examines the potential benefits and implications for breast cancer research and practice

MammoApplet: an interactive Java applet tool for manual annotation in medical imaging

Web-based applications in computational medicine have become increasingly important during the last years. The rapid growth of the World Wide Web supposes a new paradigm in the telemedicine and eHealth areas in order to assist and enhance the prevention, diagnosis and treatment of patients. Furthermore, training of radiologists and management of medical databases are also becoming increasingly important issues in the field. In this paper, we present MammoApplet , an interactive Java applet interface designed as a web-based tool. It aims to facilitate the diagnosis of new mammographic cases by providing a set of image processing tools that allow a better visualization of the images, and a set of drawing tools, used to annotate the suspicious regions. Each annotation allows including the attributes considered by the experts when issuing the final diagnosis. The overall set of overlays is stored in a database as XML files associated with the original images. The final goal is to obtain a database of already diagnosed cases for training and enhancing the performance of novice radiologistsPeer ReviewedPostprint (author's final draft

An Intelligent Data Mining System to Detect Health Care Fraud

The chapter begins with an overview of the types of healthcare fraud. Next, there is a brief discussion of issues with the current fraud detection approaches. The chapter then develops information technology based approaches and illustrates how these technologies can improve current practice. Finally, there is a summary of the major findings and the implications for healthcare practice

Machine learning methods for histopathological image analysis

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.Comment: 23 pages, 4 figure

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

Focal Spot, Spring 2006

https://digitalcommons.wustl.edu/focal_spot_archives/1102/thumbnail.jp

Breaking the Barriers to Specialty Care: Practical Ideas to Improve Health Equity and Reduce Cost - Helping Patients Engage in Specialty Care

Tremendous health outcome inequities remain in the U.S. across race and ethnicity, gender and sexual orientation, socio-economic status, and geography—particularly for those with serious conditions such as lung or skin cancer, HIV/AIDS, or cardiovascular disease.These inequities are driven by a complex set of factors—including distance to a specialist, insurance coverage, provider bias, and a patient's housing and healthy food access. These inequities not only harm patients, resulting in avoidable illness and death, they also drive unnecessary health systems costs.This 5-part series highlights the urgent need to address these issues, providing resources such as case studies, data, and recommendations to help the health care sector make meaningful strides toward achieving equity in specialty care.Top TakeawaysThere are vast inequalities in access to and outcomes from specialty health care in the U.S. These inequalities are worst for minority patients, low-income patients, patients with limited English language proficiency, and patients in rural areas.A number of solutions have emerged to improve health outcomes for minority and medically underserved patients. These solutions fall into three main categories: increasing specialty care availability, ensuring high-quality care, and helping patients engage in care.As these inequities are also significant drivers of health costs, payers, health care provider organizations, and policy makers have a strong incentive to invest in solutions that will both improve outcomes and reduce unnecessary costs. These actors play a critical role in ensuring that equity is embedded into core care delivery at scale.Part 4: "Helping Patients Engage in Specialty Care"Specialty care actors are increasingly addressing the social determinants of health with community outreach, patient navigation, and patient support services