scholarly journals SEMANTICALLY INTEROPERABLE XML DATA

2013 ◽  
Vol 07 (03) ◽  
pp. 237-255 ◽  
Author(s):  
CRISTOBAL VERGARA-NIEDERMAYR ◽  
FUSHENG WANG ◽  
TONY PAN ◽  
TAHSIN KURC ◽  
JOEL SALTZ
Keyword(s):  
Information Exchange ◽  
Medical Image ◽  
Xml Schema ◽  
Data Sources ◽  
Grid Infrastructure ◽  
Common Data Elements ◽  
Xml Data ◽  
Web Based ◽  
Semantic Annotations ◽  
Data Elements

XML is ubiquitously used as an information exchange platform for web-based applications in healthcare, life sciences, and many other domains. Proliferating XML data are now managed through latest native XML database technologies. XML data sources conforming to common XML schemas could be shared and integrated with syntactic interoperability. Semantic interoperability can be achieved through semantic annotations of data models using common data elements linked to concepts from ontologies. In this paper, we present a framework and software system to support the development of semantic interoperable XML based data sources that can be shared through a Grid infrastructure. We also present our work on supporting semantic validated XML data through semantic annotations for XML Schema, semantic validation and semantic authoring of XML data. We demonstrate the use of the system for a biomedical database of medical image annotations and markups.

scholarly journals An overview of biomedical platforms for managing research data

2021 ◽  
Author(s):  
Vivek Navale ◽  
Denis von Kaeppler ◽  
Matthew McAuliffe
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Data Reuse ◽  
Common Data Elements ◽  
Web Based ◽  
Bioinformatics Software ◽  
Share Data ◽  
Secure Cloud Computing ◽  
Data Elements ◽  
Access Services

AbstractBiomedical platforms provide the hardware and software to securely ingest, process, validate, curate, store, and share data. Many large-scale biomedical platforms use secure cloud computing technology for analyzing, integrating, and storing phenotypic, clinical, and genomic data. Several web-based platforms are available for researchers to access services and tools for biomedical research. The use of bio-containers can facilitate the integration of bioinformatics software with various data analysis pipelines. Adoption of Common Data Models, Common Data Elements, and Ontologies can increase the likelihood of data reuse. Managing biomedical Big Data will require the development of strategies that can efficiently leverage public cloud computing resources. The use of the research community developed standards for data collection can foster the development of machine learning methods for data processing and analysis. Increasingly platforms will need to support the integration of data from multiple disease area research.

scholarly journals TBI surveillance using the common data elements for traumatic brain injury: a population study

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Latha Ganti Stead ◽  
◽  
Aakash N Bodhit ◽  
Pratik Shashikant Patel ◽  
Yasamin Daneshvar ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Population Study ◽  
Common Data Elements ◽  
The Common ◽  
Data Elements

scholarly journals A DICOM dataset for evaluation of medical image de-identification

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Michael Rutherford ◽  
Seong K. Mun ◽  
Betty Levine ◽  
William Bennett ◽  
Kirk Smith ◽  
...  
Keyword(s):  
Health Information ◽  
National Cancer Institute ◽  
Medical Image ◽  
Cancer Imaging ◽  
Protected Health Information ◽  
Dicom Standard ◽  
Clinical Imaging ◽  
X Ray ◽  
Evaluation Dataset ◽  
Data Elements

AbstractWe developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM objects (a total of 1,693 CT, MRI, PET, and digital X-ray images) were selected from datasets published in the Cancer Imaging Archive (TCIA). Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM Attributes to mimic typical clinical imaging exams. The DICOM Standard and TCIA curation audit logs guided the insertion of synthetic PHI into standard and non-standard DICOM data elements. A TCIA curation team tested the utility of the evaluation dataset. With this publication, the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (the result of TCIA curation) are released on TCIA in advance of a competition, sponsored by the National Cancer Institute (NCI), for algorithmic de-identification of medical image datasets. The competition will use a much larger evaluation dataset constructed in the same manner. This paper describes the creation of the evaluation datasets and guidelines for their use.

Information Exchange (IE) Among Multi-Stakeholders for Improving Decision Making

2021 ◽  
Author(s):  
◽  
Ali Bazarah
Keyword(s):  
Decision Making ◽  
Information Exchange ◽  
Qualitative Evaluation ◽  
Decision Making Process ◽  
Actual Behavior ◽  
Good Theory ◽  
Multiple Stakeholders ◽  
Web Based ◽  
Mixed Methods Approach ◽  
Needed Information

Information Exchange (IE) is an important area of research in Information System (IS), yet there is a lack of theory that explains it. Existing studies usually borrow different theories from other fields to explain IE, but these theories describe the aspects that are associated with IE, not the actual behavior of IE. Additionally, a framework that guides the design of an IE platform to support IE among multiple stakeholders with the purpose of improving the decision-making process does not exist. To address these literature gaps, this dissertation first proposes a theory of Information Exchange (ToIE) to explain IE behavior and its impact on the decision-making process among multi-stakeholders. A qualitative evaluation of ToIE demonstrates that it meets the virtues of a good theory. Second, this dissertation develops an Information Exchange Decision Support (IEDS) framework that can guide the design of IE platforms for multiple stakeholders. The qualitative evaluation shows that the IEDS framework is useful for identifying the stakeholders, specifying the needed information to be exchanged, and maintaining the needed system factors necessary for IE. The IEDS framework is further instantiated to an IE platform named SES-IE. The SES-IE platform is a web-based application that facilitates the information exchange among scholarship organizations, employers, and students, and supports their decision-making process. The SES-IE platform was evaluated using a mixed-methods approach to measure the usability, usefulness, and satisfaction of the system. The successful instantiation of the SES-IE platform shows that the IEDS framework is useful for building an effective IE platform. This dissertation makes theoretical and practical contributions.

Abstract TP374: Data Linkage is Effective for Improving the Available Data for Stroke: An Example from the Australian Stroke Clinical Registry.

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Monique F Kilkenny ◽  
Helen M Dewey ◽  
Natasha A Lannin ◽  
Vijaya Sundararajan ◽  
Joyce Lim ◽  
...  
Keyword(s):  
Hospital Discharge ◽  
Hospital Discharge Data ◽  
Hospital Data ◽  
High Quality ◽  
Clinical Registry ◽  
Large Hospital ◽  
Common Data Elements ◽  
Discharge Data ◽  
Data Elements ◽  
Data Collections

Introduction: Multiple data collections can be a burden for clinicians. In 2009, the Australian Stroke Clinical Registry (AuSCR) was established by non-government and research organizations to provide quality of care data unavailable for acute stroke admissions. We show here the reliability of linking complimentary registry data with routinely collected hospital discharge data submitted to governmental bodies. Hypothesis: A high quality linkage with a > 90% rate is possible, but requires multiple personal identifiers common to each dataset. Methods: AuSCR identifying variables included date of birth (DoB), Medicare number, first name, surname, postcode, gender, hospital record number, hospital name and admission date. The Victorian Department of Health emergency department (ED) and hospital discharge linked dataset has most of these, with first name truncated to the first 3 digits, but no surname. Common data elements of AuSCR patients registered at a large hospital in Melbourne, Victoria (Australia) between 15 June 2009 and 31 December 2010 were submitted to undergo stepwise deterministic linkage. Results: The Victorian AuSCR sample had 818 records from 788 individuals. Three steps with 1) Medicare number, postcode, gender and DoB (80% matched); 2) hospital number/admit date; and 3) ED number/visit date were required to link AuSCR data with the ED and hospital discharge data. These led to an overall high quality linkage of >99% (782/788) of AuSCR patients, including 731/788 for ED records and 736/788 for hospital records. Conclusion: Multiple personal identifiers from registries are required to achieve reliable linkage to routinely collected hospital data. Benefits of these linked data include the ability to investigate a broader range of research questions than with a single dataset. Characters with spaces= 1941 (limit is 1950)

scholarly journals Preclinical common data elements (CDEs) for epilepsy: A joint ILAE/AES and NINDS translational initiative

2018 ◽  
Vol 3 ◽  
pp. 9-12 ◽  
Author(s):  
Helen E. Scharfman ◽  
Aristea S. Galanopoulou ◽  
Jacqueline A. French ◽  
Asla Pitkänen ◽  
Vicky Whittemore ◽  
...  
Keyword(s):  
Common Data Elements ◽  
Data Elements

scholarly journals Postmarketing Safety Study Tool: A Web Based, Dynamic, and Interoperable System for Postmarketing Drug Surveillance Studies

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
A. Anil Sinaci ◽  
Gokce B. Laleci Erturkmen ◽  
Suat Gonul ◽  
Mustafa Yuksel ◽  
Paolo Invernizzi ◽  
...  
Keyword(s):  
Clinical Research ◽  
Real Life ◽  
Critical Factor ◽  
Drug Surveillance ◽  
Safety Study ◽  
Data Interoperability ◽  
Common Data Elements ◽  
Lombardy Region ◽  
Research Activities ◽  
Data Elements

Postmarketing drug surveillance is a crucial aspect of the clinical research activities in pharmacovigilance and pharmacoepidemiology. Successful utilization of available Electronic Health Record (EHR) data can complement and strengthen postmarketing safety studies. In terms of the secondary use of EHRs, access and analysis of patient data across different domains are a critical factor; we address this data interoperability problem between EHR systems and clinical research systems in this paper. We demonstrate that this problem can be solved in an upper level with the use of common data elements in a standardized fashion so that clinical researchers can work with different EHR systems independently of the underlying information model. Postmarketing Safety Study Tool lets the clinical researchers extract data from different EHR systems by designing data collection set schemas through common data elements. The tool interacts with a semantic metadata registry through IHE data element exchange profile. Postmarketing Safety Study Tool and its supporting components have been implemented and deployed on the central data warehouse of the Lombardy region, Italy, which contains anonymized records of about 16 million patients with over 10-year longitudinal data on average. Clinical researchers in Roche validate the tool with real life use cases.

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