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Open Data for Discovery Science

Guest Editors: Andreas Holzinger, Institute for Medical Informatics, Medical University Graz, Austria and Philip Payne, Washington University in St. Louis, USA.

New Content ItemThe use of open data for discovery science has gained much attention recently as its full potential is unfolding and being explored in projects spanning all areas of healthcare research. A plethora of data sets are now available thanks to drives to make data universally accessible and usable for discovery science. However, with these advances come inherent challenges with the processing and management of ever expanding data sources. The computational and informatics tools and methods currently used in most investigational settings are often labor intensive and rely upon technologies that have not been designed to scale and support reasoning across multi-dimensional data resources. In addition, there are many challenges associated with the storage and responsible use of open data, particularly medical data, such as privacy, data protection, safety, information security and fair use of the data. There are therefore significant demands from the research community for the development of data management and analytic tools supporting heterogeneous analytic workflows and open data sources. Effective anonymisation tools are also of paramount importance to protect data security whilst preserving the usability of the data.

The purpose of this thematic series is to bring together articles reporting advances in the use of open data.

  1. Vaccination has been one of the most successful public health interventions to date, and the U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) currently contains more than 500,000 reports for post-vacci...

    Authors: Jian-Jian Ren, Tingni Sun, Yongqun He and Yuji Zhang

    Citation: BMC Medical Informatics and Decision Making 2019 19:101

    Content type: Research article

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  2. Extracting primary care information in terms of Patient/Problem, Intervention, Comparison and Outcome, known as PICO elements, is difficult as the volume of medical information expands and the health semantics...

    Authors: Samir Chabou and Michal Iglewski

    Citation: BMC Medical Informatics and Decision Making 2018 18:128

    Content type: Research article

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  3. The openEHR approach can improve the interoperability of electronic health record (EHR) through two-level modeling. Developing archetypes for the complete EHR dataset is essential for implementing a large-scal...

    Authors: Lingtong Min, Qi Tian, Xudong Lu and Huilong Duan

    Citation: BMC Medical Informatics and Decision Making 2018 18:75

    Content type: Research article

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  4. Informatics for Integrating Biology and the Bedside (i2b2) is an open source clinical data analytics platform used at over 200 healthcare institutions for querying patient data. The i2b2 platform has several c...

    Authors: Kavishwar B. Wagholikar, Pralav Dessai, Javier Sanz, Michael E. Mendis, Douglas S. Bell and Shawn N. Murphy

    Citation: BMC Medical Informatics and Decision Making 2018 18:66

    Content type: Software

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  5. The use of knowledge models facilitates information retrieval, knowledge base development, and therefore supports new knowledge discovery that ultimately enables decision support applications. Most existing wo...

    Authors: Yiqing Zhao, Nooshin J. Fesharaki, Hongfang Liu and Jake Luo

    Citation: BMC Medical Informatics and Decision Making 2018 18:61

    Content type: Research article

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  6. The performance of Computer Aided Diagnosis Systems for early melanoma detection relies mainly on quantitative evaluation of the geometric features corresponding to skin lesions. In these systems, diagnosis is...

    Authors: Agustin Sancen-Plaza, Raul Santiago-Montero, Humberto Sossa, Francisco J. Perez-Pinal, Juan J. Martinez-Nolasco and Jose A. Padilla-Medina

    Citation: BMC Medical Informatics and Decision Making 2018 18:50

    Content type: Research article

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