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Henry W Chen, Jingcheng Du, Hsing-Yi Song, Xiangyu Liu, Guoqian Jiang, Cui Tao
BACKGROUND: Today, there is an increasing need to centralize and standardize electronic health data within clinical research as the volume of data continues to balloon. Domain-specific common data elements (CDEs) are emerging as a standard approach to clinical research data capturing and reporting. Recent efforts to standardize clinical study CDEs have been of great benefit in facilitating data integration and data sharing. The importance of the temporal dimension of clinical research studies has been well recognized; however, very few studies have focused on the formal representation of temporal constraints and temporal relationships within clinical research data in the biomedical research community...
February 22, 2018: JMIR Medical Informatics
Deepak K Sharma, Harold R Solbrig, Cui Tao, Chunhua Weng, Christopher G Chute, Guoqian Jiang
BACKGROUND: Detailed Clinical Models (DCMs) have been regarded as the basis for retaining computable meaning when data are exchanged between heterogeneous computer systems. To better support clinical cancer data capturing and reporting, there is an emerging need to develop informatics solutions for standards-based clinical models in cancer study domains. The objective of the study is to develop and evaluate a cancer genome study metadata management system that serves as a key infrastructure in supporting clinical information modeling in cancer genome study domains...
June 5, 2017: Journal of Biomedical Semantics
Guoqian Jiang, Harold R Solbrig, Eric Prud'hommeaux, Cui Tao, Chunhua Weng, Christopher G Chute
Domain-specific common data elements (CDEs) are emerging as an effective approach to standards-based clinical research data storage and retrieval. A limiting factor, however, is the lack of robust automated quality assurance (QA) tools for the CDEs in clinical study domains. The objectives of the present study are to prototype and evaluate a QA tool for the study of cancer CDEs using a post-coordination approach. The study starts by integrating the NCI caDSR CDEs and The Cancer Genome Atlas (TCGA) data dictionaries in a single Resource Description Framework (RDF) data store...
2015: AMIA ... Annual Symposium Proceedings
Hua Min, Riki Ohira, Michael A Collins, Jessica Bondy, Nancy E Avis, Olga Tchuvatkina, Paul K Courtney, Richard P Moser, Abdul R Shaikh, Bradford W Hesse, Mary Cooper, Dianne Reeves, Bob Lanese, Cindy Helba, Suzanne M Miller, Eric A Ross
OBJECTIVE: In an effort to standardize behavioral measures and their data representation, the present study develops a methodology for incorporating measures found in the National Cancer Institute's (NCI) grid-enabled measures (GEM) portal, a repository for behavioral and social measures, into the cancer data standards registry and repository (caDSR). METHODS: The methodology consists of four parts for curating GEM measures into the caDSR: (1) develop unified modeling language (UML) models for behavioral measures; (2) create common data elements (CDE) for UML components; (3) bind CDE with concepts from the NCI thesaurus; and (4) register CDE in the caDSR...
July 2014: Journal of the American Medical Informatics Association: JAMIA
K Liu, A Acharya, S Alai, T K Schleyer
Anecdotal evidence suggests that, during the clinical care process, many dental practices record some data that are also collected in dental practice based research network (PBRN) studies. Since the use of existing, electronically stored data for research has multiple benefits, we investigated the overlap between research data fields used in dental PBRN studies and clinical data fields typically found in general dental records. We mapped 734 unique data elements from the Dental Information Model (DIM) to 2,487 Common Data Elements (CDE) curated by the NIDCR's PBRNs in the Cancer Data Standards Registry and Repository (caDSR)...
July 2013: Journal of Dental Research
Philipp Bruland, Bernhard Breil, Fleur Fritz, Martin Dugas
Planning case report forms for data capture in clinical trials is a labor-insensitive and not formalized process. These CRFs are often neither standardized nor using defined data elements. Metadata registries as the NCI caDSR provide the capability to create forms based on common data elements. However, an exchange of these forms into clinical trial management systems through a standardized format like CDISC ODM is currently not offered. Thus, our objectives were to develop a mapping model between NCI forms and ODM...
2012: Studies in Health Technology and Informatics
Huaqin Pan, Kimberly A Tryka, Daniel J Vreeman, Wayne Huggins, Michael J Phillips, Jayashri P Mehta, Jacqueline H Phillips, Clement J McDonald, Heather A Junkins, Erin M Ramos, Carol M Hamilton
The PhenX Toolkit provides researchers with recommended, well-established, low-burden measures suitable for human subject research. The database of Genotypes and Phenotypes (dbGaP) is the data repository for a variety of studies funded by the National Institutes of Health, including genome-wide association studies. The dbGaP requires that investigators provide a data dictionary of study variables as part of the data submission process. Thus, dbGaP is a unique resource that can help investigators identify studies that share the same or similar variables...
May 2012: Human Mutation
Jyotishman Pathak, Helen Pan, Janey Wang, Sudha Kashyap, Peter A Schad, Carol M Hamilton, Daniel R Masys, Christopher G Chute
Combining genome-wide association studies (GWAS) data with clinical information from the electronic medical record (EMR) provide unprecedented opportunities to identify genetic variants that influence susceptibility to common, complex diseases. While mining the vastness of EMR greatly expands the potential for conducting GWAS, non-standardized representation and wide variability of clinical data and phenotypes pose a major challenge to data integration and analysis. To address this requirement, we present experiences and methods developed to map phenotypic data elements from eMERGE (Electronic Medical Record and Genomics) to PhenX (Consensus Measures for Phenotypes and Exposures) and NCI's Cancer Data Standards Registry and Repository (caDSR)...
2011: AMIA Summits on Translational Science Proceedings
Guoqian Jiang, Harold R Solbrig, Christopher G Chute
The binding of controlled terminology has been regarded as important for standardization of Common Data Elements (CDEs) in cancer research. However, the potential of such binding has not yet been fully explored, especially its quality assurance aspect. The objective of this study is to explore whether there is a relationship between terminological annotations and the UMLS Semantic Network (SN) that can be exploited to improve those annotations. We profiled the terminological concepts associated with the standard structure of the CDEs of the NCI Cancer Data Standards Repository (caDSR) using the UMLS SN...
December 2011: Journal of Biomedical Informatics
Jyotishman Pathak, Janey Wang, Sudha Kashyap, Melissa Basford, Rongling Li, Daniel R Masys, Christopher G Chute
BACKGROUND: Systematic study of clinical phenotypes is important for a better understanding of the genetic basis of human diseases and more effective gene-based disease management. A key aspect in facilitating such studies requires standardized representation of the phenotype data using common data elements (CDEs) and controlled biomedical vocabularies. In this study, the authors analyzed how a limited subset of phenotypic data is amenable to common definition and standardized collection, as well as how their adoption in large-scale epidemiological and genome-wide studies can significantly facilitate cross-study analysis...
July 2011: Journal of the American Medical Informatics Association: JAMIA
Richard P Moser, Bradford W Hesse, Abdul R Shaikh, Paul Courtney, Glen Morgan, Erik Augustson, Sarah Kobrin, Kerry Y Levin, Cynthia Helba, David Garner, Marsha Dunn, Kisha Coa
Scientists are taking advantage of the Internet and collaborative web technology to accelerate discovery in a massively connected, participative environment--a phenomenon referred to by some as Science 2.0. As a new way of doing science, this phenomenon has the potential to push science forward in a more efficient manner than was previously possible. The Grid-Enabled Measures (GEM) database has been conceptualized as an instantiation of Science 2.0 principles by the National Cancer Institute (NCI) with two overarching goals: (1) promote the use of standardized measures, which are tied to theoretically based constructs; and (2) facilitate the ability to share harmonized data resulting from the use of standardized measures...
May 2011: American Journal of Preventive Medicine
S Krikov, R C Price, S A Matney, K Allen-Brady, J C Facelli
BACKGROUND: A cursory analysis of the biomedical grid literature shows that most projects emphasize data sharing and the development of new applications for the grid environment. Much less is known about the best practices for the migration of existing analytical tools into the grid environment. OBJECTIVES: To make GeneHunter available as a grid service and to evaluate the effort and best practices needed to enable a legacy application as a grid service when addressing semantic integration and using the caBIG tools...
2011: Methods of Information in Medicine
Yu Rang Park, Ju Han Kim
Achieving data interoperability between organizations relies upon agreed meaning and representation (metadata) of data. For managing and registering metadata, many organizations have built metadata registries (MDRs) in various domains based on international standard for MDR framework, ISO/IEC 11179. Following this trend, two pubic MDRs in biomedical domain have been created, United States Health Information Knowledgebase (USHIK) and cancer Data Standards Registry and Repository (caDSR), from U.S. Department of Health & Human Services and National Cancer Institute (NCI), respectively...
2010: Studies in Health Technology and Informatics
Qin Gao, Yan-lei Zhang, Zhi-yun Xie, Qi-peng Zhang, Zhang-zhi Hu
A critical factor in the advancement of biomedical research is the ease with which data can be integrated, redistributed and analyzed both within and across domains. This paper summarizes the Biomedical Information Core Infrastructure built by National Cancer Institute Center for Bioinformatics in America (NCICB). The main product from the Core Infrastructure is caCORE--cancer Common Ontologic Reference Environment, which is the infrastructure backbone supporting data management and application development at NCICB...
April 18, 2006: Beijing da Xue Xue Bao. Yi Xue Ban, Journal of Peking University. Health Sciences
Joshua Phillips, Ram Chilukuri, Gilberto Fragoso, Denise Warzel, Peter A Covitz
BACKGROUND: Robust, programmatically accessible biomedical information services that syntactically and semantically interoperate with other resources are challenging to construct. Such systems require the adoption of common information models, data representations and terminology standards as well as documented application programming interfaces (APIs). The National Cancer Institute (NCI) developed the cancer common ontologic representation environment (caCORE) to provide the infrastructure necessary to achieve interoperability across the systems it develops or sponsors...
2006: BMC Medical Informatics and Decision Making
Denise B Warzel, Christo Andonaydis, Bill McCurry, Ram Chilukuri, Sadritdin Ishmukhamedov, Peter Covitz
The NCI provides the cancer Data Standards Repository (caDSR) to support development and deployment of CDEs in cancer research. The caDSR, part of the NCI caCORE infrastructure, supports data management workflow requirements and adherence to ISO/IEC 11179 metadata standards. CDEs are developed using standard terminology from caCORE vocabulary services, and are then deployed to multi-site clinical trials data management systems. Here we describe the caDSR and how CDEs are managed and deployed in clinical research...
2003: AMIA ... Annual Symposium Proceedings
Peter A Covitz, Frank Hartel, Carl Schaefer, Sherri De Coronado, Gilberto Fragoso, Himanso Sahni, Scott Gustafson, Kenneth H Buetow
MOTIVATION: Sites with substantive bioinformatics operations are challenged to build data processing and delivery infrastructure that provides reliable access and enables data integration. Locally generated data must be processed and stored such that relationships to external data sources can be presented. Consistency and comparability across data sets requires annotation with controlled vocabularies and, further, metadata standards for data representation. Programmatic access to the processed data should be supported to ensure the maximum possible value is extracted...
December 12, 2003: Bioinformatics
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