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

Helmut Spengler1, Claudia Lang1, Tanmaya Mahapatra1

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Summary

This study introduces a new platform that simplifies managing clinical data warehouses and loading diverse data formats. It significantly reduces effort and time for researchers, making data more accessible for medical research.

Keywords:
Dockercohort selectiondata warehouseextract-transform-loadhostinghypothesis generationi2b2tranSMARTtranslational research

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Area of Science:

  • Biomedical Informatics
  • Data Science in Healthcare
  • Clinical Data Warehousing

Background:

  • Data-driven medical research relies on robust infrastructure for insights and decision support.
  • Clinical and translational data warehouses (e.g., i2b2, tranSMART) unify heterogeneous datasets for research use cases.
  • Challenges include platform complexity, time-consuming data loading, and the need for domain expertise.

Purpose of the Study:

  • To develop a platform addressing challenges in managing clinical and translational data warehouses.
  • To enhance agility in platform management and data loading processes.
  • To facilitate closer collaboration between informaticians and clinical researchers.

Main Methods:

  • Formulated system requirements for agile platform management and data loading.
  • Designed a cloud infrastructure with unified interfaces for multiple warehouse platforms.
  • Implemented a declarative, meta-loading data pipeline automating data restructuring and cleansing.

Main Results:

  • The platform supports i2b2 and tranSMART with integrated security.
  • The data-loading pipeline successfully processed previously un-loadable data without extensive preprocessing.
  • Configuration file sizes were reduced by up to 22x for tranSMART and 1135x for i2b2, significantly lowering effort.

Conclusions:

  • The developed platform substantially reduces effort in managing clinical data warehouses and loading complex data formats.
  • Compact configuration files facilitate iterative refinement of data representations.
  • The open-source platform offers a cloud-based solution for the research community.