Integrated Data Repository Toolkit (IDRT)

Bauer, Christian R.; Ganslandt, Thomas; Baum, Benjamin; Christoph, Jan; Engel, Igor; Löbe, Matthias; Mate, Sebastian; Stäubert, Sebastian; Drepper, Johannes; Prokosch, Hans‐Ulrich; Winter, Alfred; Sax, Ulrich

doi:10.3414/me15-01-0082

Cited by 28 publications

(7 citation statements)

References 27 publications

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“…Moreover, for i2b2 only, there are other loading tools available. The most comprehensive is the IDRT Import and Mapping Tool [ 26 ]. The tool supports various import formats, such as CSV files; provides access to structured query language databases, such as Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) [ 44 , 45 ]; and provides direct support for CDMs, that are, for example, used for billing purposes.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, when deploying such systems in production environments, additional aspects such as transport encryption and password management need to be considered. These and further functionalities are not supported by existing cloud-based deployment solutions for i2b2 and tranSMART, such as the Integrated Data Repository Toolkit (IDRT) [ 26 ], i2b2 Quickstart [ 27 ], or the prebuilt images available on Docker Hub [ 28 ] (see the Discussion section for an in-depth comparison).…”

Section: Methodsmentioning

confidence: 99%

“…Populating i2b2 and tranSMART with data is cumbersome and requires significant expertise regarding the underlying database schema and how both systems use it. For this reason, several tools have been developed to simplify this process, including tranSMART-ETL [ 33 ], tMDataLoader [ 34 ], transmart-batch [ 35 ], Integrated Curation Environment (ICE) [ 36 ], IDRT [ 26 ], transmart-copy [ 37 ], and TranSMART data curation toolkit (tmtk) [ 38 ]. However, none of these tools fulfill the requirements needed to implement agility (see the Discussion section for an in-depth comparison).…”

Section: Methodsmentioning

confidence: 99%

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Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation

Spengler¹,

Lang²,

Mahapatra³

et al. 2020

JMIR Med Inform

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Background Modern data-driven medical research provides new insights into the development and course of diseases and enables novel methods of clinical decision support. Clinical and translational data warehouses, such as Informatics for Integrating Biology and the Bedside (i2b2) and tranSMART, are important infrastructure components that provide users with unified access to the large heterogeneous data sets needed to realize this and support use cases such as cohort selection, hypothesis generation, and ad hoc data analysis. Objective Often, different warehousing platforms are needed to support different use cases and different types of data. Moreover, to achieve an optimal data representation within the target systems, specific domain knowledge is needed when designing data-loading processes. Consequently, informaticians need to work closely with clinicians and researchers in short iterations. This is a challenging task as installing and maintaining warehousing platforms can be complex and time consuming. Furthermore, data loading typically requires significant effort in terms of data preprocessing, cleansing, and restructuring. The platform described in this study aims to address these challenges. Methods We formulated system requirements to achieve agility in terms of platform management and data loading. The derived system architecture includes a cloud infrastructure with unified management interfaces for multiple warehouse platforms and a data-loading pipeline with a declarative configuration paradigm and meta-loading approach. The latter compiles data and configuration files into forms required by existing loading tools, thereby automating a wide range of data restructuring and cleansing tasks. We demonstrated the fulfillment of the requirements and the originality of our approach by an experimental evaluation and a comparison with previous work. Results The platform supports both i2b2 and tranSMART with built-in security. Our experiments showed that the loading pipeline accepts input data that cannot be loaded with existing tools without preprocessing. Moreover, it lowered efforts significantly, reducing the size of configuration files required by factors of up to 22 for tranSMART and 1135 for i2b2. The time required to perform the compilation process was roughly equivalent to the time required for actual data loading. Comparison with other tools showed that our solution was the only tool fulfilling all requirements. Conclusions Our platform significantly reduces the efforts required for managing clinical and translational warehouses and for loading data in various formats and structures, such as complex entity-attribute-value structures often found in laboratory data. Moreover, it facilitates the iterative refinement of data representations in the target platforms, as the required configuration files are very compact. The quantitative measurements presented are consistent with our experiences of significantly reduced efforts for building warehousing platforms in close cooperation with medical researchers. Both the cloud-based hosting infrastructure and the data-loading pipeline are available to the community as open source software with comprehensive documentation.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation

Spengler¹,

Lang²,

Mahapatra³

et al. 2020

JMIR Med Inform

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show abstract

“…All relevant processes from building and testing to data capture and quality control to the standardized export to biomedical analyses are covered within the research group. Although this infrastructure proved successful during the last 8 years, some further development concerning data integration, data filtering and data analysis 16 as well as adaptations to the current central UMG biobanking infrastructure 17 18 19 have to be addressed in the future.…”

Section: Methodsmentioning

confidence: 99%

Neoadjuvant Therapy in Rectal Cancer - Biobanking of Preoperative Tumor Biopsies

Nietert

Gusky

et al. 2016

Sci Rep

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Translational research relies on high-quality biospecimens. In patients with rectal cancer treated preoperatively with radiochemotherapy tissue based analyses are challenging. To assess quality challenges we analyzed tissue samples taken over the last years in a multicenter setting. We retrospectively evaluated overall 197 patients of the CAO/ARO/AIO-94- and 04-trial with locally advanced rectal cancer that were biopsied preoperatively at the University Medical Center Goettingen as well as in 10 cooperating hospitals in Germany. The cellular content of tumor, mucosa, stroma, necrosis and the amount of isolated DNA and RNA as well as the RNA integrity number (RIN) as quality parameters were evaluated. A high RNA yield (p = 2.75e–07) and the content of tumor (p = 0.004) is significantly associated to high RIN-values, whereas a high content of mucosa (p = 0.07) shows a trend and a high amount of necrosis (p = 0.01) is significantly associated with RNA of poor quality. Correlating biopsies from Goettingen and the cooperating centers showed comparable tumor content results. By taking small sized biopsies we could assess a clear correlation between a good RNA quality and a high amount of RNA and tumor cells. These results also indicate that specimens collected at different centers are of comparable quality.

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“…The diagram of the proposed methodology was presented in Figure 1. The function can be represented by an effective coding/decoding procedure (Demir et al, 2018;Wallace, 2016;Bauer et al, 2016) Figure 1: The new general data protection approach.…”

Section: The New Data Protection Approachmentioning

confidence: 99%

Technical and Legal Aspects of Database's Security in the Light of Implementation of General Data Protection Regulation

Drąg

Szymura

2018

CBUP

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In the modern era, information is not only a valuable commodity, but also a potential source of threat, especially when it comes to personal data. The implementation of the General Data Protection Regulation seeks to unify regulations and safeguards in a same manner across the EU. The following paper surveys how the legal aspects of GDPR influence the existing technical framework of databases containing personal data. In this research we want to show if the already existing technical infrastructure and safeguards implemented in databases containing personal data are sufficient and if not, if implementing new ways of protecting of data will require creating entire new system of databases or only changing of existing framework. Therefore, we combine an analysis of legal texts with a technical analysis of existing and newly implemented safeguards. While the GDPR doesn’t answer what safeguards should be implemented (in the spirit of technological neutrality), the notion of pseudonymisation of the data is strongly advocated through the Regulation. In our paper we tried to show the algorithm, which create a pseudonymisation function that can change personal data into generic data with the possibility to reverse that process ad utilise data after de-pseudonymisation. Implementing safeguards based on the following function create a more safe environment for data safekeeping, while give nearly immediate access to data for authorised person, who can reverse pseudonymisation and transform generic data once more into personal data.

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Integrated Data Repository Toolkit (IDRT)

Cited by 28 publications

References 27 publications

Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation

Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation

Neoadjuvant Therapy in Rectal Cancer - Biobanking of Preoperative Tumor Biopsies

Technical and Legal Aspects of Database's Security in the Light of Implementation of General Data Protection Regulation

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