scholarly journals Standardized Informatics Computing Platform for Advancing Biomedical Discovery Through Data Sharing

2018 ◽  
Author(s):  
Vivek Navale ◽  
Michelle Ji ◽  
Evan McCreedy ◽  
Tsega Gebremichael ◽  
Alison Garcia ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Biomedical Research ◽  
Computing System ◽  
Research Data ◽  
Research Needs ◽  
Unique Identifier ◽  
Common Data Elements ◽  
Informatics System ◽  
Data Elements ◽  
User Friendly

AbstractObjectiveThe goal is to develop a standardized informatics computing system that can support end-to-end research data lifecycle management for biomedical research applications.Materials and MethodsDesign and implementation of biomedical research informatics computing system (BRICS) is demonstrated. The system architecture is modular in design with several integrated tools: global unique identifier, validation, upload, download and query tools that support user friendly informatics system capability.ResultsBRICS instances were deployed to support research for improvements in diagnosis of traumatic brain injury, biomarker discovery for Parkinson’s Disease, the National Ophthalmic Disease Genotyping and Phenotyping network, the informatics core for the Center for Neuroscience and Regenerative Medicine, the Common Data Repository for Nursing Science, Global Rare Diseases Patient Registry, and National Institute of Neurological Disorders and Stroke Clinical Informatics system for trials and research.DiscussionData deidentification is conducted by using global unique identifier methodology. No personally identifiable information exists on the BRICS supported repositories. The Data Dictionary provides defined Common Data Elements and Unique Data Elements, specific to each of the BRICS instance that enables Query Tool to search through research data. All instances are supported by the Medical Imaging Processing, statistical analysis R, and Visualization software program.ConclusionThe BRICS core modules can be easily adapted for various biomedical research needs thereby reducing cost in developing new instances for additional biomedical research needs. It provides user friendly tools for researchers to query and aggregate genetic, phenotypic, clinical and medical imaging data. Data sets are findable, accessible and reusable for researchers to foster new research on various diseases.

scholarly journals Development of an informatics system for accelerating biomedical research.

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 1430 ◽  
Author(s):  
Vivek Navale ◽  
Michele Ji ◽  
Olga Vovk ◽  
Leonie Misquitta ◽  
Tsega Gebremichael ◽  
...  
Keyword(s):  
Biomedical Research ◽  
Science Research ◽  
Computing System ◽  
Study Data ◽  
Research Management ◽  
Data Repository ◽  
Biomedical Data ◽  
Unique Identifier ◽  
Data Repositories ◽  
Common Data Elements

The Biomedical Research Informatics Computing System (BRICS) was developed to support multiple disease-focused research programs. Seven service modules are integrated together to provide a collaborative and extensible web-based environment. The modules—Data Dictionary, Account Management, Query Tool, Protocol and Form Research Management System, Meta Study, Data Repository and Globally Unique Identifier —facilitate the management of research protocols, to submit, process, curate, access and store clinical, imaging, and derived genomics data within the associated data repositories. Multiple instances of BRICS are deployed to support various biomedical research communities focused on accelerating discoveries for rare diseases, Traumatic Brain Injury, Parkinson’s Disease, inherited eye diseases and symptom science research. No Personally Identifiable Information is stored within the data repositories. Digital Object Identifiers are associated with the research studies. Reusability of biomedical data is enhanced by Common Data Elements (CDEs) which enable systematic collection, analysis and sharing of data. The use of CDEs with a service-oriented informatics architecture enabled the development of disease-specific repositories that support hypothesis-based biomedical research.

Abstract WP203: National Institute of Neurological Disorders and Stroke Common Data Elements: Public Review and Updates to the Stroke Recommendations

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Katelyn Gay ◽  
Damon Collie ◽  
Muniza Sheikh ◽  
Joy Esterlitz ◽  
Jeffrey Saver ◽  
...  
Keyword(s):  
Data Collection ◽  
Clinical Research ◽  
Neurological Disorders ◽  
Vital Signs ◽  
Cerebral Aneurysms ◽  
Research Data ◽  
Common Data Element ◽  
Common Data Elements ◽  
Stroke Research ◽  
Data Elements

Objective: The National Institute of Neurological Disorders and Stroke (NINDS) initiated the Common Data Element (CDE) project to provide standardized clinical research data collection formats that increase the efficiency and effectiveness of studies and reduce start-up time, as well as improve data quality and facilitate and accelerate data sharing. In 2010, Stroke-specific CDEs were posted on the NINDS CDE website. The Stroke Oversight Committee (OC) reviewed Core CDEs in 2015; and in 2018, recommended that Stroke CDEs undergo a comprehensive review and update to Version 2.0. Background: In August 2018, a Stroke V2.0 Working Group (WG) consisting of over 50 worldwide subject matter experts was convened by NINDS. The WG was asked to review all current Stroke CDEs and subarachnoid hemorrhage and unruptured cerebral aneurysms (SAH) CDEs (developed in 2017) for harmonization and inclusion within Stroke V2.0. Methods: The Stroke V2.0 WG divided into eight domain-specific subgroups: Biospecimens, Biomarkers, and Laboratory Tests; Hospital Course and Acute Therapies; Imaging; Long Term Therapies; Medical History and Prior Health Status; Outcomes and Endpoints; Stroke Presentation and Vital Signs; and Stroke Types and Subtypes. Subgroups met regularly to review, revise and add to the existing Stroke CDEs based on developments in stroke research. Following an internal WG review, a public review of the draft updates will be held. The WG will consider public feedback before V2.0 is finalized. The Stroke OC plans to review the project status at the 2020 International Stroke Conference. Results: The Stroke V2.0 CDE recommendations will include updated and new template case report forms, data dictionaries, instrument informational documents and guideline documents. The updates will reflect the current state of science, streamline CDE recommendations, and incorporate SAH CDEs. Stroke V2.0 CDEs will be available on the NINDS CDE website in 2020. Conclusions: The NINDS CDEs are periodically revised as research progresses. Through the update of the Stroke CDEs to V2.0, the initiative strives to maintain the utility of CDEs as a valuable clinical research resource. NINDS encourages use of CDEs to standardize research data collection across studies.

scholarly journals A Standards based Ontological Approach to Information Handling for use by Organizations Providing Human Tissue for Research

2008 ◽  
Vol 6 ◽  
pp. 117693510800600 ◽  
Author(s):  
Mary E. Edgerton ◽  
Carl Morrison ◽  
Virginia A. LiVolsi ◽  
Christopher A. Moskaluk ◽  
Stephen J. Qualman ◽  
...  
Keyword(s):  
Human Tissue ◽  
Tissue Bank ◽  
State University ◽  
Common Data Elements ◽  
Educational Backgrounds ◽  
Informatics System ◽  
Specific Protocol ◽  
Ontological Approach ◽  
The Ohio State University ◽  
Data Elements

Tissue resources have become an important component of the infrastructure of institutions as well as companies performing biomedical research. Such tissue resources may be in the model of a bank, collecting a limited type of tissues and processing and storing them following a specific protocol. Such banks or archives may be associated with a clinical study or may function indepedently. An alternative type of tissue resource is utilized by many institutions and cancer centers. In this model, the investigator specifies the methods by which selected tissues are to be collected, processed and stored. In such a “prospective model”, initially developed at the University of Alabama at Birmingham and the Ohio State University in the late 1970's and adopted by the Cooperative Human Tissue Network in 1986, specific types of tissues are not collected unless requested by an investigator. At some sites, both a prospective and an archival (bank) model are followed. This article describes an informatics approach needed to support a prospective tissue resource. It is by necessity more complicated than a model which supports a tissue bank but also can be used by a tissue bank. Of great importance is the approach to vocabulary and common data elements needed to support the informatics system of a prospective tissue resource, especially if the informatics system is to be used by a variety of personnel with greatly varying educational backgrounds.

scholarly journals Common Data Elements and Federal Interagency Traumatic Brain Injury Research Informatics System for TBI Research

2015 ◽  
Vol 33 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Hilaire J. Thompson ◽  
Monica S. Vavilala ◽  
Frederick P. Rivara
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Specific Treatment ◽  
Lessons Learned ◽  
Secondary Injury ◽  
Common Data Elements ◽  
Informatics System ◽  
Data Elements ◽  
Injury Research ◽  
Development And Evolution

Despite increased attention to traumatic brain injury (TBI), there remains no specific treatment and available interventions focus rather on the prevention of secondary injury. One of the reasons posited for the lack of a successful therapy is the amalgamation of various types of injuries under the same severity category in clinical trials. Informatics approaches have been suggested as a means to develop an improved classification system for TBI. As a result of federal interagency efforts, common data elements (CDEs) for TBI have now been developed. Further, the Federal Interagency Traumatic Brain Injury Research Informatics System (FITBIR) has been created and is now available for TBI researchers to both add and retrieve data. This chapter will discuss the goals, development, and evolution of the CDEs and FITBIR and discuss how these tools can be used to support TBI research. A specific exemplar using the CDEs and lessons learned from working with the CDEs and FITBIR are included to aid future researchers.

scholarly journals Development of an informatics system for accelerating biomedical research.

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1430
Author(s):  
Vivek Navale ◽  
Michele Ji ◽  
Olga Vovk ◽  
Leonie Misquitta ◽  
Tsega Gebremichael ◽  
...  
Keyword(s):  
Biomedical Research ◽  
Service Oriented Architecture ◽  
Research Management ◽  
Data Element ◽  
Unique Identifier ◽  
Data Interoperability ◽  
Common Data Element ◽  
Data Repositories ◽  
Functional Capabilities ◽  
Informatics System

Biomedical translational research can benefit from informatics system that support the confidentiality, integrity and accessibility of data.  Such systems require functional capabilities for researchers to securely submit data to designated biomedical repositories. Reusability of data is enhanced by the availability functional capabilities that ensure confidentiality, integrity and access of data. A biomedical research system was developed by combining common data element methodology with a service-oriented architecture to support multiple disease focused research programs. Seven service modules are integrated together to provide a collaborative and extensible web-based environment. The modules - Data Dictionary, Account Management, Query Tool, Protocol and Form Research Management System, Meta Study, Repository Manager and globally unique identifier (GUID) facilitate the management of research protocols, submitting and curating data (clinical, imaging, and derived genomics) within the associated data repositories. No personally identifiable information is stored within the repositories. Data is made findable by use of digital object identifiers that are associated with the research studies. Reuse of data is possible by searching through volumes of aggregated research data across multiple studies. The application of common data element(s) methodology for development of content-based repositories leads to increase in data interoperability that can further hypothesis-based biomedical research.

scholarly journals Using an artificial neural network to map cancer common data elements to the biomedical research integrated domain group model in a semi-automated manner

2019 ◽  
Vol 19 (S7) ◽  
Author(s):  
Robinette Renner ◽  
Shengyu Li ◽  
Yulong Huang ◽  
Ada Chaeli van der Zijp-Tan ◽  
Shaobo Tan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Biomedical Research ◽  
Experimental Results ◽  
Training Data ◽  
Alignment Algorithm ◽  
Common Data Elements ◽  
Match Rate ◽  
Artificial Neural ◽  
Data Elements

Abstract Background The medical community uses a variety of data standards for both clinical and research reporting needs. ISO 11179 Common Data Elements (CDEs) represent one such standard that provides robust data point definitions. Another standard is the Biomedical Research Integrated Domain Group (BRIDG) model, which is a domain analysis model that provides a contextual framework for biomedical and clinical research data. Mapping the CDEs to the BRIDG model is important; in particular, it can facilitate mapping the CDEs to other standards. Unfortunately, manual mapping, which is the current method for creating the CDE mappings, is error-prone and time-consuming; this creates a significant barrier for researchers who utilize CDEs. Methods In this work, we developed a semi-automated algorithm to map CDEs to likely BRIDG classes. First, we extended and improved our previously developed artificial neural network (ANN) alignment algorithm. We then used a collection of 1284 CDEs with robust mappings to BRIDG classes as the gold standard to train and obtain the appropriate weights of six attributes in CDEs. Afterward, we calculated the similarity between a CDE and each BRIDG class. Finally, the algorithm produces a list of candidate BRIDG classes to which the CDE of interest may belong. Results For CDEs semantically similar to those used in training, a match rate of over 90% was achieved. For those partially similar, a match rate of 80% was obtained and for those with drastically different semantics, a match rate of up to 70% was achieved. Discussion Our semi-automated mapping process reduces the burden of domain experts. The weights are all significant in six attributes. Experimental results indicate that the availability of training data is more important than the semantic similarity of the testing data to the training data. We address the overfitting problem by selecting CDEs randomly and adjusting the ratio of training and verification samples. Conclusions Experimental results on real-world use cases have proven the effectiveness and efficiency of our proposed methodology in mapping CDEs with BRIDG classes, both those CDEs seen before as well as new, unseen CDEs. In addition, it reduces the mapping burden and improves the mapping quality.

scholarly journals Long-term preservation of biomedical research data

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1353 ◽  
Author(s):  
Vivek Navale ◽  
Matthew McAuliffe
Keyword(s):  
Big Data ◽  
Research Data ◽  
Digital Data ◽  
Biomedical Science ◽  
Data Provenance ◽  
Common Data Elements ◽  
Open Archival Information System ◽  
Data Elements ◽  
Long Term Preservation

Genomics and molecular imaging, along with clinical and translational research have transformed biomedical science into a data-intensive scientific endeavor. For researchers to benefit from Big Data sets, developing long-term biomedical digital data preservation strategy is very important. In this opinion article, we discuss specific actions that researchers and institutions can take to make research data a continued resource even after research projects have reached the end of their lifecycle. The actions involve utilizing an Open Archival Information System model comprised of six functional entities: Ingest, Access, Data Management, Archival Storage, Administration and Preservation Planning. We believe that involvement of data stewards early in the digital data life-cycle management process can significantly contribute towards long term preservation of biomedical data. Developing data collection strategies consistent with institutional policies, and encouraging the use of common data elements in clinical research, patient registries and other human subject research can be advantageous for data sharing and integration purposes. Specifically, data stewards at the onset of research program should engage with established repositories and curators to develop data sustainability plans for research data. Placing equal importance on the requirements for initial activities (e.g., collection, processing, storage) with subsequent activities (data analysis, sharing) can improve data quality, provide traceability and support reproducibility. Preparing and tracking data provenance, using common data elements and biomedical ontologies are important for standardizing the data description, making the interpretation and reuse of data easier. The Big Data biomedical community requires scalable platform that can support the diversity and complexity of data ingest modes (e.g. machine, software or human entry modes). Secure virtual workspaces to integrate and manipulate data, with shared software programs (e.g., bioinformatics tools), can facilitate the FAIR (Findable, Accessible, Interoperable and Reusable) use of data for near- and long-term research needs.

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

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)

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