An overview of biomedical platforms for managing research data

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.

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EPID-23. PURSUIT OF AN INTERNATIONAL LANGUAGE OF GLIOMA RESEARCH: COMMON DATA ELEMENTS FOR THE LONGITUDINAL STUDY OF ADULT MALIGNANT GLIOMA

Neuro-Oncology ◽

10.1093/neuonc/noz175.323 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi79-vi79

Author(s):

Laila Poisson ◽

M C M Kouwenhoven ◽

James Snyder ◽

Kristin Alfaro-Munoz ◽

Manpreet Kaur ◽

...

Keyword(s):

Longitudinal Study ◽

Malignant Glioma ◽

Web Application ◽

Data Capture ◽

Data Repositories ◽

Common Data Elements ◽

Research Perspectives ◽

Share Data ◽

Data Elements ◽

Meta Analyses

Abstract As an uncommon cancer, clinical and translational studies of glioma rely on multi-center collaborations, confirmatory studies, and meta-analyses. Unfortunately, interpretation of results across studies is hampered by the absence of uniformly coded clinical data. Common Data Elements (CDE) represent a set of clinical features for which the language has been standardized for consistent data capture across studies, institutions and registries. We constructed CDE for the longitudinal study of adult malignant glioma. To identify the minimum set of CDE needed to describe the clinical course of glioma, we surveyed clinical standards, ongoing trials, published studies, and data repositories for frequently used data elements. We harmonized the identified clinical variables, filled in gaps, and structured them in a modular schema, defining CDE for patient demographics, medical history, diagnosis, surgery, chemotherapy, radiotherapy, other treatments, and outcomes. Multidisciplinary experts from the Glioma Longitudinal AnalySiS (GLASS) consortium, representing clinical, molecular, and data research perspectives, were consulted regarding CDE. The validity and capture feasibility of the CDE were assessed through harmonization across published studies, then validated with single institution retrospective chart abstraction. The refined CDE library is implemented in the Research Electronic Data Capture (REDCap) System, a secure web application for building and managing online surveys and databases. The work was motivated by the GLASS consortium, which supports the aggregation and analysis of complex genetic datasets used to define molecular trajectories for glioma. The goal is that modular REDCap implementation of CDE allows broad adoption in glioma research. To accommodate novel aspects, the CDE sets can be expanded through additional modules. In contrast, for efficient initiation of focused studies, subsets of CDE can be selected. Broad adoption of CDE will improve the ability to compare results and share data between studies, thereby maximizing the value of existing data sources and small patient populations.

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SEMANTICALLY INTEROPERABLE XML DATA

International Journal of Semantic Computing ◽

10.1142/s1793351x13500037 ◽

2013 ◽

Vol 07 (03) ◽

pp. 237-255 ◽

Cited By ~ 2

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.

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Common Data Elements, Scalable Data Management Infrastructure, and Analytics Workflows for Large-Scale Neuroimaging Studies

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.682495 ◽

2021 ◽

Vol 12 ◽

Author(s):

Rayus Kuplicki ◽

James Touthang ◽

Obada Al Zoubi ◽

Ahmad Mayeli ◽

Masaya Misaki ◽

...

Keyword(s):

Data Management ◽

Large Scale ◽

Laboratory Data ◽

Imaging Data ◽

Common Data Elements ◽

Level Data ◽

Reproducible Analysis ◽

Brain Imaging Data ◽

Data Elements ◽

The Brain

Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.

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Comparison Study of Different NoSQL and Cloud Paradigm for Better Data Storage Technology

Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-5972-6.ch015 ◽

2018 ◽

pp. 312-343

Author(s):

Pankaj Lathar ◽

K. G. Srinivasa ◽

Abhishek Kumar ◽

Nabeel Siddiqui

Keyword(s):

Cloud Computing ◽

Data Management ◽

Data Storage ◽

High Performance ◽

Large Scale ◽

Web Based ◽

Storage Technology ◽

Data Store ◽

Challenges And Opportunities ◽

User Data

Advancements in web-based technology and the proliferation of sensors and mobile devices interacting with the internet have resulted in immense data management requirements. These data management activities include storage, processing, demand of high-performance read-write operations of big data. Large-scale and high-concurrency applications like SNS and search engines have appeared to be facing challenges in using the relational database to store and query dynamic user data. NoSQL and cloud computing has emerged as a paradigm that could meet these requirements. The available diversity of existing NoSQL and cloud computing solutions make it difficult to comprehend the domain and choose an appropriate solution for a specific business task. Therefore, this chapter reviews NoSQL and cloud-system-based solutions with the goal of providing a perspective in the field of data storage technology/algorithms, leveraging guidance to researchers and practitioners to select the best-fit data store, and identifying challenges and opportunities of the paradigm.

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The TRACK-TBI Approach: Redefining Severity and Outcome Assessments

Neurotrauma ◽

10.1093/med/9780190279431.003.0008 ◽

2018 ◽

pp. 81-100

Author(s):

John K. Yue ◽

Ethan A. Winkler ◽

Hansen Deng ◽

Amy J. Markowitz ◽

Kevin K. W. Wang ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Pilot Study ◽

Data Collection ◽

Large Scale ◽

Diagnostic Tools ◽

Common Data Elements ◽

Diagnostic Approaches ◽

Prospective Trials ◽

Data Elements

Advances in traumatic brain injury (TBI) research have been limited by imprecise classification and diagnostic approaches and insensitive outcome measures. The National Institute of Neurological Disorders and Stroke TBI Common Data Elements (CDEs) project aimed to standardize data collection across TBI research, discover new diagnostic tools, and develop a multidimensional outcomes endpoint sensitive to differential profiles of recovery. Progress from implementing the TBI CDEs is described via the Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) study. Refinements to the TBI CDEs are incorporated into several ongoing large-scale prospective trials comprising a comprehensive, harmonized dataset capable of refining severity markers and outcome endpoints.

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Common Data Elements, Scalable Data Management Infrastructure and Analytics Workflows for Large-scale Neuroimaging Studies

10.1101/2021.03.16.21253726 ◽

2021 ◽

Author(s):

Rayus Kuplicki ◽

James Touthang ◽

Obada Al Zoubi ◽

Ahmad Mayeli ◽

Masaya Misaki ◽

...

Keyword(s):

Data Management ◽

Large Scale ◽

Laboratory Data ◽

Imaging Data ◽

Common Data Elements ◽

Level Data ◽

Reproducible Analysis ◽

Brain Imaging Data ◽

Data Elements ◽

The Brain

Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.

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Radiologic common data elements rates in pediatric mild traumatic brain injury

Neurology ◽

10.1212/wnl.0000000000008488 ◽

2019 ◽

Vol 94 (3) ◽

pp. e241-e253 ◽

Cited By ~ 1

Author(s):

Andrew R. Mayer ◽

Daniel M. Cohen ◽

Christopher J. Wertz ◽

Andrew B. Dodd ◽

Jody Shoemaker ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Mild Traumatic Brain Injury ◽

Incidental Findings ◽

Large Scale ◽

Structural Mri ◽

Mri Findings ◽

Common Data Elements ◽

Data Elements

ObjectiveThe nosology for classifying structural MRI findings following pediatric mild traumatic brain injury (pmTBI) remains actively debated. Radiologic common data elements (rCDE) were developed to standardize reporting in research settings. However, some rCDE are more specific to trauma (probable rCDE). Other more recently proposed rCDE have multiple etiologies (possible rCDE), and may therefore be more common in all children. Independent cohorts of patients with pmTBI and controls were therefore recruited from multiple sites (New Mexico and Ohio) to test the dual hypothesis of a higher incidence of probable rCDE (pmTBI > controls) vs similar rates of possible rCDE on structural MRI.MethodsPatients with subacute pmTBI (n = 287), matched healthy controls (HC; n = 106), and orthopedically injured (OI; n = 71) patients underwent imaging approximately 1 week postinjury and were followed for 3–4 months.ResultsProbable rCDE were specific to pmTBI, occurring in 4%–5% of each sample, rates consistent with previous large-scale CT studies. In contrast, prevalence rates for incidental findings and possible rCDE were similar across groups (pmTBI vs OI vs HC). The prevalence of possible rCDE was also the only finding that varied as a function of site. Possible rCDE and incidental findings were not associated with postconcussive symptomatology or quality of life 3–4 months postinjury.ConclusionCollectively, current findings question the trauma-related specificity of certain rCDE, as well how these rCDE are radiologically interpreted. Refinement of rCDE in the context of pmTBI may be warranted, especially as diagnostic schema are evolving to stratify patients with structural MRI abnormalities as having a moderate injury.

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Privacy-Preserving and Secure Cloud Computing: A Case of Large-Scale Nonlinear Programming

IEEE Transactions on Cloud Computing ◽

10.1109/tcc.2021.3099720 ◽

2021 ◽

pp. 1-1

Author(s):

Wei Du ◽

Ang Li ◽

Qinghua Li ◽

Pan Zhou

Keyword(s):

Cloud Computing ◽

Nonlinear Programming ◽

Large Scale ◽

Privacy Preserving ◽

Secure Cloud Computing

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Using Web-based Testing for Large-Scale Assessment

PsycEXTRA Dataset ◽

10.1037/e421172005-001 ◽

2013 ◽

Author(s):

Laura S. Hamilton ◽

Stephen P. Klein ◽

William Lorie

Keyword(s):

Large Scale ◽

Web Based ◽

Large Scale Assessment ◽

Scale Assessment

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Impact of COVID-19 on Nuclear Medicine in Germany, Austria and Switzerland: An International Survey in April 2020

Nuklearmedizin ◽

10.1055/a-1163-3096 ◽

2020 ◽

Vol 59 (04) ◽

pp. 294-299 ◽

Cited By ~ 4

Author(s):

Lutz S. Freudenberg ◽

Ulf Dittmer ◽

Ken Herrmann

Keyword(s):

Nuclear Medicine ◽

Health Systems ◽

Personal Protective Equipment ◽

Large Scale ◽

Diagnostic Procedures ◽

Protective Equipment ◽

Current Crisis ◽

Web Based ◽

Public Versus Private ◽

The Impact

Abstract Introduction Preparations of health systems to accommodate large number of severely ill COVID-19 patients in March/April 2020 has a significant impact on nuclear medicine departments. Materials and Methods A web-based questionnaire was designed to differentiate the impact of the pandemic on inpatient and outpatient nuclear medicine operations and on public versus private health systems, respectively. Questions were addressing the following issues: impact on nuclear medicine diagnostics and therapy, use of recommendations, personal protective equipment, and organizational adaptations. The survey was available for 6 days and closed on April 20, 2020. Results 113 complete responses were recorded. Nearly all participants (97 %) report a decline of nuclear medicine diagnostic procedures. The mean reduction in the last three weeks for PET/CT, scintigraphies of bone, myocardium, lung thyroid, sentinel lymph-node are –14.4 %, –47.2 %, –47.5 %, –40.7 %, –58.4 %, and –25.2 % respectively. Furthermore, 76 % of the participants report a reduction in therapies especially for benign thyroid disease (-41.8 %) and radiosynoviorthesis (–53.8 %) while tumor therapies remained mainly stable. 48 % of the participants report a shortage of personal protective equipment. Conclusions Nuclear medicine services are notably reduced 3 weeks after the SARS-CoV-2 pandemic reached Germany, Austria and Switzerland on a large scale. We must be aware that the current crisis will also have a significant economic impact on the healthcare system. As the survey cannot adapt to daily dynamic changes in priorities, it serves as a first snapshot requiring follow-up studies and comparisons with other countries and regions.

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