A scalable approach for critical care data extraction and analysis in an academic medical center
Using clinical routine data for secondary research is challenging and requires an interdisciplinary team. We developed a scalable approach that automates steps for cohort identification, data extraction and common data pre-processing steps. Additionally, we facilitate data harmonization, integration and consult on typical data analysis scenarios, machine learning algorithms and visualizations in dashboards.
Authors
- Sebastian Daniel Boie (ORCID: https://orcid.org/0000-0001-7993-7901)
- Camille Guinemer (ORCID: https://orcid.org/0000-0002-7982-0073)
- Fabian Praßer (ORCID: https://orcid.org/0000-0003-3172-3095)
- Peter Brunecker (ORCID: https://orcid.org/0000-0003-1107-1982)
- Jon Barrenetxea (ORCID: https://orcid.org/0000-0002-5466-553X)
- Niklas Giesa (ORCID: https://orcid.org/0000-0003-0808-3966)
- Felix Balzer (ORCID: https://orcid.org/0000-0003-1575-2056)
- Falk Meyer-Eschenbach (ORCID: https://orcid.org/0009-0000-9813-0249)
- Michael Krämer (ORCID: https://orcid.org/0000-0002-3089-6827)
- Fabian Schreiber
- Stefan Haufe
Institutions
- Humboldt-Universität zu Berlin (DE)
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
- Charité - Universitätsmedizin Berlin (DE)
Publication Details
- Journal
- International Journal of Medical Informatics
- Published
- 2024-09-06
- DOI
- https://doi.org/10.1016/j.ijmedinf.2024.105611
- Citations
- 8
- Primary Topic
- Electronic Health Records Systems
- Type
- article
- Field-Weighted Citation Impact
- 5.66