Statistical biases due to anonymization evaluated in an open clinical dataset from COVID-19 patients

Anonymization has the potential to foster the sharing of medical data. State-of-the-art methods use mathematical models to modify data to reduce privacy risks. However, the degree of protection must be balanced against the impact on statistical properties. We studied an extreme case of this trade-of...

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Detalles Bibliográficos
Autores principales: Koll, Carolin E. M., Hopff, Sina M., Meurers, Thierry, Lee, Chin Huang, Kohls, Mirjam, Stellbrink, Christoph, Thibeault, Charlotte, Reinke, Lennart, Steinbrecher, Sarah, Schreiber, Stefan, Mitrov, Lazar, Frank, Sandra, Miljukov, Olga, Erber, Johanna, Hellmuth, Johannes C., Reese, Jens-Peter, Steinbeis, Fridolin, Bahmer, Thomas, Hagen, Marina, Meybohm, Patrick, Hansch, Stefan, Vadász, István, Krist, Lilian, Jiru-Hillmann, Steffi, Prasser, Fabian, Vehreschild, Jörg Janne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Analysis
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9769467/
https://www.ncbi.nlm.nih.gov/pubmed/36543828
http://dx.doi.org/10.1038/s41597-022-01669-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9769467/
https://www.ncbi.nlm.nih.gov/pubmed/36543828
http://dx.doi.org/10.1038/s41597-022-01669-9

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