Databases containing very large amounts of SNP (Single Nucleotide Polymorphism) data are now freely available for researchers interested in medical and/or population genetics applications. While many of these SNP repositories have implemented data retrieval tools for general-purpose mining, these alone cannot cover the broad spectrum of needs of most medical and population genetics studies. To address this limitation, we have built in-house customized data marts from the raw data provided by the largest public databases. In particular, for population genetics analysis based on genotypes we have built a set of data processing scripts that deal with raw data coming from the major SNP variation databases (e.g. HapMap, Perlegen), stripping them into single genotypes and then grouping them into populations, then merged with additional complementary descriptive information extracted from dbSNP. This allows not only in-house standardization and normalization of the genotyping data retrieved from different repositories, but also the calculation of statistical indices from simple allele frequency estimates to more elaborate genetic differentiation tests within populations, together with the ability to combine population samples from different databases. The present study demonstrates the viability of implementing scripts for handling extensive datasets of SNP genotypes with low computational costs, dealing with certain complex issues that arise from the divergent nature and configuration of the most popular SNP repositories. The information contained in these databases can also be enriched with additional information obtained from other complementary databases, in order to build a dedicated data mart. Updating the data structure is straightforward, as well as permitting easy implementation of new external data and the computation of supplementary statistical indices of interest.

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Viability of in-house datamarting approaches for population genetics analysis of SNP genotypes

Spiraling health care costs in the United States are driving institutions to continually address the challenge of optimizing the use of scarce resources. One of the first steps towards optimizing resources is to utilize capacity effectively. For hospital capacity planning problems such as allocation of inpatient beds, computer simulation is often the method of choice. One of the more difficult aspects of using simulation models for such studies is the creation of a manageable set of patient types to include in the model. The objective of this paper is to demonstrate the potential of using data mining techniques, specifically clustering techniques such as K-means, to help guide the development of patient type definitions for purposes of building computer simulation or analytical models of patient flow in hospitals. Using data from a hospital in the Midwest this study brings forth several important issues that researchers need to address when applying clustering techniques in general and specifically to hospital data.

Data Mining to Support Simulation Modeling of Patient Flow in Hospitals

In 1998, the University of Michigan Health System embarked upon the design, development, and implementation of an enterprise-wide data warehouse, intending to use prioritized business questions to drive its design and implementation. Because of the decentralized nature of the academic health system and the development team's inability to identify and prioritize those institutional business questions, however, a bottom-up approach was used to develop the enterprise-wide data warehouse. Specific important data sets were identified for inclusion, and the technical team designed the system with an enterprise view and architecture rather than as a series of data marts. Using this incremental approach of adding data sets, institutional leaders were able to experience and then further define successful use of the integrated data made available to them. Even as requests for the use and expansion of the data warehouse outstrip the resources assigned for support, the data warehouse has become an integral component of the institution's information management strategy. The authors discuss the approach, process, current status, and successes and failures of the data warehouse.

Development of a data warehouse at an academic health system: knowing a place for the first time.

In field such as Cardiology, data used for clinical studies is not only alphanumeric, but can also be composed of images or signals. Multimedia data warehouse then must be studied in order to provide an efficient environment for the analysis of this data. The analysis environment must include appropriate processing methods in order to compute or extract the knowledge embedded into raw data. Traditional multidimensional models have a static structure which members of dimensions are computed in a unique way. However, multimedia data is often characterized by descriptors that can be obtained by various computation modes. We define these computation modes as "functional versions" of the descriptors. We propose a Functional Multiversion Multidimensional Model by integrating the concept of "version of dimension." This concept defines dimensions with members computed according to various functional versions. This new approach integrates different computation modes of these members into the proposed model, in order to allow the user to select the best representation of data. In this paper, a conceptual model is formally defined and a prototype for this study is presented. A multimedia data warehouse in the medical field has been implemented on a therapeutic study on acute myocardial infarction

Multimedia data warehouses: a multiversion model and a medical application

One of the major obstacles to using organizational data for mining and knowledge discovery is that, in most cases, it is not amenable for mining in its natural form. Using a data set from a large tertiary-care hospital, we provide strong empirical evidence that data enhancement by the introduction of new attributes, along with judicious aggregation of existing attributes, results in higher-quality knowledge discovery. Interestingly, we also found that there is a differential impact of data set enhancements on the performance of different data mining algorithms. We define and use several measures, including entropy, rule complexity and resonance, to evaluate the quality and usefulness of the knowledge discovered.

Exploiting data preparation to enhance mining and knowledge discovery

In this article we describe the evolution and architecture of a data mart developed to address the modeling and analysis needs of healthcare operations analysts. More specifically, the data mart is used in projects relating to demand analysis, forecasting, capacity planning, and service system design for a healthcare system consisting of a large tertiary care hospital and a smaller community hospital. The primary focus of the mart is on the detailed movement of inpatients through each hospital, although most component data tables include outpatient information such as emergency center visits, surgical cases, cardiac catheterization cases, and short-stay visits. We show that the data mart goes well beyond consolidating data from different sources by including a number of complex, precalculated fields, data structures, and function libraries that are specific to the needs of operations analysts. We discuss several outstanding and challenging design issues that should be of interest to the data warehouse vendor community.

West M

Papers

A data mart for operations analysis.