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

Finding Temporal Patterns in Noisy Longitudinal Data: A Study in Diabetic Retinopathy

This paper describes an approach to temporal pattern mining using the concept of user defined temporal prototypes to define the nature of the trends of interests. The temporal patterns are defined in terms of sequences of support values associated with identified frequent patterns. The prototypes are defined mathematically so that they can be mapped onto the temporal patterns. The focus for the advocated temporal pattern mining process is a large longitudinal patient database collected as part of a diabetic retinopathy screening programme, The data set is, in itself, also of interest as it is very noisy (in common with other similar medical datasets) and does not feature a clear association between specific time stamps and subsets of the data. The diabetic retinopathy application, the data warehousing and cleaning process, and the frequent pattern mining procedure (together with the application of the prototype concept) are all described in the paper. An evaluation of the frequent pattern mining process is also presented

Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure

On-line analytical processing

On-line analytical processing (OLAP) describes an approach to decision support, which aims to extract knowledge from a data warehouse, or more specifically, from data marts. Its main idea is providing navigation through data to non-expert users, so that they are able to interactively generate ad hoc queries without the intervention of IT professionals. This name was introduced in contrast to on-line transactional processing (OLTP), so that it reflected the different requirements and characteristics between these classes of uses. The concept falls in the area of business intelligence.Peer ReviewedPostprint (author's final draft

Fatal Injuries and Nonfatal Occupational Injuries and Illnesses Involving Insects, Arachnids, and Mites

[Excerpt] This issue of Beyond the Numbers article examines fatal and nonfatal workplace injuries and illnesses related to insects, arachnids, and mites using data from two Bureau of Labor Statistics (BLS) sources: the Census of Fatal Occupational Injuries (CFOI) and the Survey of Occupational Injuries and Illnesses (SOII). CFOI data used here are from 2003 to 2010 and aggregated to support extended analysis. SOII data are from 2008 to 2010. BLS began publishing national SOII estimates for state and local government in 2008, so that period was chosen to keep the coverage of CFOI and SOII data in this study as comparable as possible. For this article, the term insects refers to the entire category, for short

Catch That Bus: Reverse-Commute Challenges Facing Low Income Inner-City Residents of Onondaga County

[Excerpt] Employer concerns about labor shortages for entry-level positions in the suburbs and outlying city neighborhoods prompted county planners to ask Cornell ILR to conduct this study. We organized a series of focus groups with low-income inner-city residents who commute to the suburbs or outlying city neighborhoods and work in health services, hospitality, or warehousing; we also spoke with several supervisors and a transportation planner. We found four major transportation challenges: limited service at non-standard times; out-of-synch schedules; off-schedule and off-route buses; and poorly located bus stops. We highlight several transportation initiatives that have been tried in other communities and propose a series of recommendations that transit planners, the transit company, and employers might consider in order to mitigate the reverse-commute challenges in ways that would benefit all stakeholders

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