Data Quality in the ProVal-MS Study: Challenges and Lessons Learned.
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| Title: | Data Quality in the ProVal-MS Study: Challenges and Lessons Learned. |
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| Authors: | Pallaoro P; Data Integration Center, School of Medicine and Health, Technical University of Munich, Munich, Germany.; Technical University of Munich, TUM School of Medicine and Health, Institute for AI and Informatics in Medicine (AIIM), TUM University Hospital., Bilger S; MeDIC Ulm and Institute of Medical Systems Biology, University of Ulm, Ulm, Germany., Boeker M; Technical University of Munich, TUM School of Medicine and Health, Institute for AI and Informatics in Medicine (AIIM), TUM University Hospital., Budeus M; Data Integration Center, School of Medicine and Health, Technical University of Munich, Munich, Germany.; Technical University of Munich, TUM School of Medicine and Health, Institute for AI and Informatics in Medicine (AIIM), TUM University Hospital., Dehmelt V; Technical University of Munich, TUM School of Medicine and Health, Institute for AI and Informatics in Medicine (AIIM), TUM University Hospital., Hagedorn M; Medical Data Integration Center (MeDICLMU), University Hospital, LMU Munich, Munich., Kohlmayer F; Bitcare GmbH, Munich, Germany., Mansmann U; Institute for Medical Information Processing, Biometry and Epidemiology, LMU, Munich, Germany., Rohr SO; Digital Medicine, University Hospital of Augsburg, Augsburg, Germany., Sailer B; Medical Data Integration Center, University Hospital Tübingen, Tübingen, Germany., Spengler H; Data Integration Center, School of Medicine and Health, Technical University of Munich, Munich, Germany., Buchka S; Institute for Medical Information Processing, Biometry and Epidemiology, LMU, Munich, Germany. |
| Source: | Studies in health technology and informatics [Stud Health Technol Inform] 2026 May 21; Vol. 336, pp. 1262-1266. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: IOS Press Country of Publication: Netherlands NLM ID: 9214582 Publication Model: Print Cited Medium: Internet ISSN: 1879-8365 (Electronic) Linking ISSN: 09269630 NLM ISO Abbreviation: Stud Health Technol Inform Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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