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At least 235 records · Page 13Linked to original sources
PubMed2000

Racial disparities in access to simultaneous pancreas-kidney transplantation in the United States.

The purpose of our study is to assess the extent of racial differences in the access to simultaneous pancreas-kidney (SPK) transplantation and evaluate the potential influence of socioeconomic factors on access to transplantation. We performed a retrospective analysis of the US Renal Data System and United Network for Organ Sharing data on all patients with end-stage renal disease (ESRD) due to diabetes mellitus from 1988 to 1996 (n = 562, 814), including all dialysis, wait list, and transplant patients. Racial differences in incidence, prevalence, insurance coverage, employment status, and transplantation rates were calculated. Caucasians had the highest prevalence of ESRD caused by type 1 diabetes (73%), followed by blacks (22%), Hispanics (3%), Native Americans (2%), and others (<1%). Both blacks and Native Americans increased their annual incidence of ESRD caused by insulin-dependent diabetes mellitus by 10% compared with only a 3.5% increase in Caucasians, whereas incidence rates increased annually by almost 8% for both blacks and Native Americans compared with a 3% increase for Caucasians. However, Caucasians received 92% of all SPK transplants, whereas all other racial groups combined received a disproportionate minority of the remaining transplants. Lack of private insurance and unemployment status were associated with annual changes in both incidence of ESRD caused by type 1 diabetes and SPK transplant rates. In conclusion, we observed striking racial disparities for access to SPK transplantation in the United States today, which may be related to employment status, access to private insurance, and subsequent health care. Our preliminary data support current efforts to encourage Medicare and Medicaid coverage for all patients requiring SPK transplantation regardless of racial or financial status.

Black or African American↗
PubMed1992

Structural design of hidden Markov model speech recognizer using multivalued phonetic features: comparison with segmental speech units.

A novel approach to speech recognition, on the basis of a multidimensional multivalued phonetic-feature description of speech signals, is presented and evaluated. The hidden Markov model (HMM) framework is used to provide the recognition algorithm, which assumes that the underlying Markov chain tracks the temporal evolution of the features. It is shown that this approach can naturally accommodate such coarticulatory effects as feature spreading and formant transition in the functionality of the recognizer, and can provide a high degree of acoustic data sharing that makes effective use of training data. Use of phonetic features as the basic speech units creates a framework where the Markov model's state topology in the recognizer can be designed with guidance of detailed speech knowledge. Details of such a design for a stop consonant-vowel vocabulary are described. Experimental results on the task of speaker-dependent stop consonant discrimination, evaluated from speech data from a total of ten male and five female speakers, demonstrate effectiveness of this feature-based recognizer. Over the 15 speakers, the error rates were shown to be reduced by 23%, 37%, 42%, and 38%, respectively, compared with the conventional HMM-based recognition methods using words, phonemes, allophones, and microsegments as the primary speech units.

Communication↗
PubMed2006

An XML-based system for synthesis of data from disparate databases.

Diverse data sets have become key building blocks of translational biomedical research. Data types captured and referenced by sophisticated research studies include high throughput genomic and proteomic data, laboratory data, data from imagery, and outcome data. In this paper, the authors present the application of an XML-based data management system to support integration of data from disparate data sources and large data sets. This system facilitates management of XML schemas and on-demand creation and management of XML databases that conform to these schemas. They illustrate the use of this system in an application for genotype-phenotype correlation analyses. This application implements a method of phenotype-genotype correlation based on phylogenetic optimization of large data sets of mouse SNPs and phenotypic data. The application workflow requires the management and integration of genomic information and phenotypic data from external data repositories and from the results of phenotype-genotype correlation analyses. Our implementation supports the process of carrying out a complex workflow that includes large-scale phylogenetic tree optimizations and application of Maddison's concentrated changes test to large phylogenetic tree data sets. The data management system also allows collaborators to share data in a uniform way and supports complex queries that target data sets.

Animals↗
PubMed2004

Database development in toxicogenomics: issues and efforts.

The marriage of toxicology and genomics has created not only opportunities but also novel informatics challenges. As with the larger field of gene expression analysis, toxicogenomics faces the problems of probe annotation and data comparison across different array platforms. Toxicogenomics studies are generally built on standard toxicology studies generating biological end point data, and as such, one goal of toxicogenomics is to detect relationships between changes in gene expression and in those biological parameters. These challenges are best addressed through data collection into a well-designed toxicogenomics database. A successful publicly accessible toxicogenomics database will serve as a repository for data sharing and as a resource for analysis, data mining, and discussion. It will offer a vehicle for harmonizing nomenclature and analytical approaches and serve as a reference for regulatory organizations to evaluate toxicogenomics data submitted as part of registrations. Such a database would capture the experimental context of in vivo studies with great fidelity such that the dynamics of the dose response could be probed statistically with confidence. This review presents the collaborative efforts between the European Molecular Biology Laboratory-European Bioinformatics Institute ArrayExpress, the International Life Sciences Institute Health and Environmental Science Institute, and the National Institute of Environmental Health Sciences National Center for Toxigenomics Chemical Effects in Biological Systems knowledge base. The goal of this collaboration is to establish public infrastructure on an international scale and examine other developments aimed at establishing toxicogenomics databases. In this review we discuss several issues common to such databases: the requirement for identifying minimal descriptors to represent the experiment, the demand for standardizing data storage and exchange formats, the challenge of creating standardized nomenclature and ontologies to describe biological data, the technical problems involved in data upload, the necessity of defining parameters that assess and record data quality, and the development of standardized analytical approaches.

Animals↗
PubMed2003

The cell-centered database: a database for multiscale structural and protein localization data from light and electron microscopy.

The creation of structured shared data repositories for molecular data in the form of web-accessible databases like GenBank has been a driving force behind the genomic revolution. These resources serve not only to organize and manage molecular data being created by researchers around the globe, but also provide the starting point for data mining operations to uncover interesting information present in the large amount of sequence and structural data. To realize the full impact of the genomic and proteomic efforts of the last decade, similar resources are needed for structural and biochemical complexity in biological systems beyond the molecular level, where proteins and macromolecular complexes are situated within their cellular and tissue environments. In this review, we discuss our efforts in the development of neuroinformatics resources for managing and mining cell level imaging data derived from light and electron microscopy. We describe the main features of our web-accessible database, the Cell Centered Database (CCDB; http://ncmir.ucsd.edu/CCDB/), designed for structural and protein localization information at scales ranging from large expanses of tissue to cellular microdomains with their associated macromolecular constituents. The CCDB was created to make 3D microscopic imaging data available to the scientific community and to serve as a resource for investigating structural and macromolecular complexity of cells and tissues, particularly in the rodent nervous system.

Brain↗
PubMedDate not supplied

Managing troubled data: coastal data partnerships smooth data integration.

Understanding the ecology, condition, and changes of coastal areas requires data from many sources. Broad-scale and long-term ecological questions, such as global climate change, biodiversity, and cumulative impacts of human activities, must be addressed with databases that integrate data from several different research and monitoring programs. Various barriers, including widely differing data formats, codes, directories, systems, and metadata used by individual programs, make such integration troublesome. Coastal data partnerships, by helping overcome technical, social, and organizational barriers, can lead to a better understanding of environmental issues, and may enable better management decisions. Characteristics of successful data partnerships include a common need for shared data, strong collaborative leadership, committed partners willing to invest in the partnership, and clear agreements on data standards and data policy. Emerging data and metadata standards that become widely accepted are crucial. New information technology is making it easier to exchange and integrate data. Data partnerships allow us to create broader databases than would be possible for any one organization to create by itself.

Conservation of Natural Resources↗
PubMed2006

Constructing a semantically enriched biomedical service space: a paradigm with bioinformatics resources.

Biomedical applications are becoming increasingly reliant on resource integration and information exchange within global solution frameworks that offer seamless connectivity and data sharing in distributed environments. Resource autonomy and data heterogeneity are the most important impediments towards this potential. Aiming to overcome these limitations, we propose an implementation of the service-oriented model towards the construction of an open, semantically enriched biomedical service space that enables advanced service registration, selection and access capabilities, as well as service interoperability. The proposed system is realised by defining service annotation ontologies and applying software agent technology as the means for service registration, matchmaking and interfacing in a Grid environment. The applicability of the envisioned biomedical service space is illustrated on a set of bioinformatics resources, addressing computational identification of protein-coding genes.

Computational Biology↗
PubMed1995

Dynamic patient data bases: the foundation of an integrated approach to outcome measures for the healthcare professionals.

In recent years there has been a tremendous need among healthcare professionals to assess the effectiveness, efficiency, and appropriateness of the patient care services being provided through criteria-based outcome and program evaluation. Although the need for a tool which could evaluate the effectiveness of patient care is widely recognized, such an undertaking has been severely limited due to the lack of any automated means to collect and analyze patient data on a routine, continuous basis within a clinical setting. We have developed and implemented at Mineral Springs Hospital, Banff, Alberta an integrated and automated hospital information system that not only continuously collects administrative, financial, and patient data, but also contains an intelligent component for automated outcome measure and program evaluation. The system collects various non-duplicated data elements from each routine work process within the facility on a continuous basis. Through the creation of a dynamic patient database, data is transformed into information--a powerful decision support tool. The system provides flexible user-defined reports in patient-specific resource utilization, direct and/or indirect specific financial costs, result reporting of each intervention, service provided and user-defined criteria-based outcome, and program evaluation. The system design incorporates expert rules, dynamic data entry forms, quantitative models, and user-defined access control. Using information derived from the dynamic common database, managers and front-line clinicians can easily evaluate and modify management decisions or careplans on a macro or micro level. An external review is planned to evaluate whether the system has helped the assessment of effectiveness, efficiency and appropriateness of healthcare services being provided at the hospital. The fundamental concept behind the system design is that the patient is the center of activity for data collection. The system provides the answers to the 5 W's (who, what, where, when, and why) together with intervention and service result reports. A dynamic common patient database is the center of the system and is accessible to all with proper authorization. Common data elements are collected from routine work flow without extra data entry and this information is subsequently shared. Data collection is a continuous process. We believe that every process is the outcome of another sub-process or event. The design of the dynamic patient database incorporates patient-specific costing and outcome evaluation, user-defined flexible data entry forms, user-defined access control, outcome evaluation rules and information semantic rules. Such a patient database would provide the flexibility needed to accommodate diverse methodologies to evaluate outcomes whether it they be medical, cost, access and/or other combination of measures. The system was developed on a PC-based Network technology, using FOXPRO (XBase) as the database development tool incorporating advanced technology such as distributed processing and fault tolerant computing. We chose PC-based technology because it is economical, having relatively low maintenance costs and requires no major dependency on vendors. The developed system produces patient-specific reports with many dimensions. The reports are user-defined. The system reports general data, CMG, RGN, LOS, Expected LOS, and other user-defined demographic data. Resource utilization, financial costs, and result reportings are produced together with rule-based outcome assessments of any type of measures, including, but not limited to, pre-set functional/health goals, user satisfaction, clinicianUs text or codified comments etc. It provides the framework for continually capturing data at a practical, work-flow level. The incorporation of a dynamic patient database as the driving forece of an integrated, rule-based administration, financial and patient data system will provdie the tools for healthcar

Databases, Factual↗
PubMedDate not supplied

Confidentiality issues for medical data miners.

The first task in any medical data mining effort is ensuring patient confidentiality. In the past, most data mining efforts ensured confidentiality by the dubious policy of withholding their raw data from colleagues and the public. A cursory review of medical informatics literature in the past decade reveals that much of what we have "learned" consists of assertions derived from confidential datasets unavailable for anyone's review. Without access to the original data, it is impossible to validate or improve upon a researcher's conclusions. Without access to research data, we are asked to accept findings as an act of faith, rather than as a scientific conclusion. This special issue of Artificial Intelligence in Medicine is devoted to medical data mining. The medical data miner has an obligation to conduct valid research in a way that protects human subjects. Today, data miners have the technical tools to merge large data collections and to distribute queries over disparate databases. In order to include patient-related data in shared databases, data miners will need methods to anonymize and deidentify data. This article reviews the human subject risks associated with medical data mining. This article also describes some of the innovative computational remedies that will permit researchers to conduct research AND share their data without risk to patient or institution.

Computer Security↗
PubMed2006

Systematic planning of patient records for cooperative care and multicenter research.

PURPOSE: The purpose of this paper is to introduce a method for systematically planning patient records for structured data entry that can be used in cooperative environments (e.g. cooperative care, multicenter trials) in a way that enables multipurpose use and shared data entry. METHODS: Design research, formal logic. RESULTS: The method suggests five steps: analyze the prevailing documentation infrastructure, provide terminology management system (TMS), provide documentation management system (DMS), plan the logical architecture, provide all necessary tools. CONCLUSIONS: The era of eHealth enables cooperative care and collaborative documentation. This can only be efficient if a multiple use and shared entry of data is realized. The task of the medical informatics community is to plan these environments systematically especially in complex environments which are enabled by emerging technologies.

Biomedical Research↗
PubMed2006

Mobilizing the base of neuroscience data: the case of neuronal morphologies.

Despite the explosive growth of bioinformatics, data sharing has not yet become routine in neuroscience, possibly because of several broad-spanning issues, from data heterogeneity to privacy regulations. We present the case of neuronal morphology as an ideal example of shareable data. Drawing from recent experience, we argue that the tremendous research potential of existing (and largely unused) digital reconstructions should diffuse any reticence to sharing this type of data.

Animals↗
PubMed1995

Can one patient record accommodate the diversity of specialized care?

Despite a quarter century of developments, few specialists directly use a computerized patient record, that fully replaces the paper chart. Because of the diversity of domains in specialized care, medical decision-making and the continuity of care may suffer from scattering of patient data over various records. The challenge was to develop a computerized patient record, that would be versatile enough to tailor it to specific needs, while keeping it uniform enough to permit physicians to share data on the same patient. In our CPR, the key that reconciles versatility with uniformity lies in the design of the data model. The CPR consists of a mother record with specialized sub-records, that all share the same data model. A physician can enlarge his scope for decision-making by consulting other specialized records on the same patient or by viewing the combined information of all sub-records without the need to convert data or to familiarize himself with different interfaces.

Humans↗
PubMed2004

Specific clinical and brain MRI features in mentally retarded patients with mutations in the Oligophrenin-1 gene.

Oligophrenin-1 (OPHN-1) gene disruption is known as responsible for so called "non-specific" X-linked mental retardation (MR) Billuart et al. [1998: Nature 392:923-926]. In order to search for a possible specific clinical and radiological profile for mutation in the OPHN-1 gene, clinical and 3D brain MRI studies were performed in the two families with a known mutation in OPHN-1 reported so far: a 19-year-old female with an X;12 balanced translocation encompassing OPHN-1, and four affected males of family MRX60 sharing a frameshift mutation in OPHN-1. Clinical data shared by affected individuals were neonatal hypotonia with motor delay but no obvious ataxia, marked strabismus, early onset complex partial seizures, and moderate to severe MR. Brain MRIs performed in three individuals exhibited a specific vermian dysgenesis including an incomplete sulcation of anterior and posterior vermis with the most prominent defect in lobules VI and VII. In addition, a non-specific cerebral cortico-subcortical atrophy was also observed. These clinical and radiological features suggest a distinct clinico-radiological syndrome. These preliminary data need to be confirmed in other families and will be helpful for further targeted mutation screening of the OPHN-1 gene in male patients with similar clinico-radiological features. In addition, OPHN-1 inactivation should be considered as a relevant model of developmental vermis disorganization, leading to a better understanding of the possible role of the cerebellum in MR.

Adult↗
PubMed2006

Challenges and opportunities in proteomics data analysis.

Accurate, consistent, and transparent data processing and analysis are integral and critical parts of proteomics workflows in general and for biomarker discovery in particular. Definition of common standards for data representation and analysis and the creation of data repositories are essential to compare, exchange, and share data within the community. Current issues in data processing, analysis, and validation are discussed together with opportunities for improving the process in the future and for defining alternative workflows.

Databases, Protein↗
PubMed2005

The Stanford Microarray Database accommodates additional microarray platforms and data formats.

The Stanford Microarray Database (SMD) (http://smd.stanford.edu) is a research tool for hundreds of Stanford researchers and their collaborators. In addition, SMD functions as a resource for the entire biological research community by providing unrestricted access to microarray data published by SMD users and by disseminating its source code. In addition to storing GenePix (Axon Instruments) and ScanAlyze output from spotted microarrays, SMD has recently added the ability to store, retrieve, display and analyze the complete raw data produced by several additional microarray platforms and image analysis software packages, so that we can also now accept data from Affymetrix GeneChips (MAS5/GCOS or dChip), Agilent Catalog or Custom arrays (using Agilent's Feature Extraction software) or data created by SpotReader (Niles Scientific). We have implemented software that allows us to accept MAGE-ML documents from array manufacturers and to submit MIAME-compliant data in MAGE-ML format directly to ArrayExpress and GEO, greatly increasing the ease with which data from SMD can be published adhering to accepted standards and also increasing the accessibility of published microarray data to the general public. We have introduced a new tool to facilitate data sharing among our users, so that datasets can be shared during, before or after the completion of data analysis. The latest version of the source code for the complete database package was released in November 2004 (http://smd.stanford.edu/download/), allowing researchers around the world to deploy their own installations of SMD.

California↗
PubMed2001

International surveillance networks and principles of collaboration.

In the face of the multiplication and the development of international surveillance networks for communicable diseases, many questions on the transmission of personal data and information arise. The confidential nature of shared data and their disclosure internationally within and outside the network are therefore potential sources for conflicts. To resolve these problems, Enter-net developed its 'Collaboration Principles' intended to apply to all the participants of the network and to all others potentially involved. These principles propose solutions to questions related to the access to databases created within the framework of the network, to the quality and confidentiality of circulating data, to the individual responsibility in the identification of an incriminated product in case of an outbreak, and even to the transmission of information outside the network. These principles, which are to be regularly reviewed, are primarily aimed at optimising early detection and management of foodborne outbreaks, and at taking the necessary measures for public health. Considering the nature of the problems, however, some of these principles are also of relevance to other surveillance networks.

Communicable Disease Control↗
PubMedDate not supplied

Promoting interactions with basic scientists and clinicians: the NIA Alzheimer's Disease Data Coordinating Center.

To benefit Alzheimer's disease research, a central data co-ordinating centre (CDCC) is planned that will systematically collect data from 27 Alzheimer's disease centres (ADCs) located nationwide. This CDCC will combine, analyse and disseminate epidemiologic, demographic, clinical and neuropathological data to researchers from the ADCs and the broader scientific community. New and larger scale collaborative studies on Alzheimer's disease will be possible through this centre. Since 1 July 1997, an interim data co-ordinating centre (IDCC) has been serving as the agent of the ADCs to begin the data sharing process until a permanent CDCC is established. The data collected by the IDCC are limited to administrative information and to indexing of specimens and clinical material, with future plans for the transfer of the data collected to the CDCC once it is established.

Aged↗
PubMedDate not supplied

An assessment of discharge planning models: communication in referrals for home care.

A home care referral generated upon hospital discharge communicates essential patient care information, links service providers, and facilitates continuity of patient care. The literature however, reveals that communication efforts are often inadequate and may even fail in this system. The purpose of this research was to examine whether various discharge planning models employed by hospitals resulted in differences in the quantity and quality of communication about patients referred for home health care. Kelly and McClelland's (1985) typology of discharge planning models provided the framework for the study. Using instruments designed specifically for this project, six referring hospitals' functional discharge planning models were labeled and 300 closed home care records of referrals were reviewed to ascertain the amount and type of data transmitted. Results indicated that only slightly more than half of the data recommended by the literature was actually transferred and that data was primarily background in nature. The hospital discharge planning model did make a significant difference in the amount and type of data shared, with liaison nurses sending the greatest amount of data. However, both conceptually and in practice, the discharge planning models simply describe an allocation of responsibility among various health personnel. They are not operational models in the traditional sense.

Communication↗