Cancer Surveillance using Data Warehousing, Data Mining, and Decision Support Systems

This article discusses how data warehousing, data mining, and decision support systems can reduce the national cancer burden or the oral complications of cancer therapies, especially as related to oral and pharyngeal cancers. An information system is presented that will deliver the necessary information technology to clinical, administrative, and policy researchers and analysts in an effective and efficient manner. The system will deliver the technology and knowledge that users need to readily: (1) organize relevant claims data, (2) detect cancer patterns in general and special populations, (3) formulate models that explain the patterns, and (4) evaluate the efficacy of specified treatments and interventions with the formulations. Such a system can be developed through a proven adaptive design strategy, and the implemented system can be tested on State of Maryland Medicaid data (which includes women, minorities, and children)

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

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 Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses

A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses

To Greener Pastures: An Action Research Study on the Environmental Sustainability of Humanitarian Supply Chains

Purpose: While humanitarian supply chains (HSCs) inherently contribute to social sustainability by alleviating the suffering of afflicted communities, their unintended adverse environmental impact has been overlooked hitherto. This paper draws upon contingency theory to synthesize green practices for HSCs, identify the contingency factors that impact on greening HSCs and explore how focal humanitarian organizations (HOs) can cope with such contingency factors. Design/methodology/approach: Deploying an action research methodology, two-and-a-half cycles of collaboration between researchers and a United Nations agency were completed. The first half-cycle developed a deductive greening framework, synthesizing extant green practices from the literature. In the second and third cycles, green practices were adopted/customized/developed reflecting organizational and contextual contingency factors. Action steps were implemented in the HSC for prophylactics, involving an operational mix of disaster relief and development programs. Findings: First, the study presents a greening framework that synthesizes extant green practices in a suitable form for HOs. Second, it identifies the contingency factors associated with greening HSCs regarding funding environment, stakeholders, field of activity and organizational management. Third, it outlines the mechanisms for coping with the contingency factors identified, inter alia, improving the visibility of headquarters over field operations, promoting collaboration and resource sharing with other HOs as well as among different implementing partners in each country, and working with suppliers for greener packaging. The study advances a set of actionable propositions for greening HSCs. Practical implications: Using an action research methodology, the study makes strong practical contributions. Humanitarian practitioners can adopt the greening framework and the lessons learnt from the implementation cycles presented in this study. Originality/value: This is one of the first empirical studies to integrate environmental sustainability and HSCs using an action research methodology