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عنوان انگلیسی مقاله:
Biomedical and Clinical Research Data Management
ترجمه فارسی عنوان مقاله:
مدیریت داده های تحقیقات زیست پزشکی و بالینی
Sciencedirect - Elsevier - Systems Medicine: Integrative Qualitative and Computational Approaches, (2020) 1-12. doi:10.1016/B978-0-12-801238-3.11621-6
Matthias Ganzinger ,Enrico Glaab,Jules Kerssemakers ,Sven Nahnsen , Ulrich Sax,Nadine Sarah Schaadt, Matthieu-P Schapranow,Thorsten Tiede,
Systems medicine describes an interdisciplinary approach in medicine with the aim of improving disease prevention, diagnosis,
targeted treatment, and prognosis (Apweiler et al., 2018). Often, statistical, mathematical, and computational concepts of systems
biology are translated to systems medicine for clinical use (Bauer et al., 2017). This approach typically requires large amounts of
structured clinical and biomedical data covering the respective disease and patients (Gietzelt et al., 2016a). Thus, it is important to
have efficient procedures and policies in place to prepare the data from different data sources. For research projects in systems
medicine, some of the data are generated as part of the project, while others were generated beforehand, often for other purposes, e.g.
in clinical routine and in different legal context. Therefore, one of the first steps of a project is to ensure the availability of the data
needed. This task not only includes the process of assembling data files from various sources. After retrieval of the data, they typically
have to be checked and filtered for their quality (Huebner et al., 2016), converted into an appropriate format, pre-processed, and
harmonized into common formats that reflect standardized data definitions and ontologies (Krishnankutty et al., 2012).
In addition, biomedical research often relies on patient-related data, requiring additional steps like checking the permission to
use the data for the intended purpose or de-identifying the data—usually based on informed consent or special legislation.
While these steps seem to be straightforward and common to most research projects, many projects re-implement customized
solutions to build up their own infrastructure and cope with the data management challenges. Since projects usually focus on a biomedical research question, the effort of data preparation, harmonization, and management is often underestimated and
scientists with a data or computer science background are often invited to the project only at a later stage.