Representativeness of a German AI-enabled data network for secondary epidemiological analysis based on electronic health records
The high level of agreement for the variables examined indicates the representativeness of the ML dataset in comparison to the DESTATIS data. This finding paves the way for future epidemiological studies based on big data, which were previously unavailable in research.
Authors
- Inga Marte Charlott Seuthe (ORCID: https://orcid.org/0000-0002-0295-5447)
- S. Holger Eichhorn
- Josef Ladenbauer (ORCID: https://orcid.org/0000-0002-1727-7431)
- André Zimmermann
- Mehdi Dastur
- Ralph Brinks
- Franz Niklas Mitze
- Fritz Wagner
- Jonas Jae-Hyun Park
Institutions
- Witten/Herdecke University (DE)
- Philips (Germany) (DE)
Publication Details
- Journal
- PLoS ONE
- Published
- 2026-01-06
- DOI
- https://doi.org/10.1371/journal.pone.0339647
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00