Výsledky vyhledávání - "Louisa Schmalhorst"

Akademický článek

Legal aspects of privacy-enhancing technologies in genome-wide association studies and their impact on performance and feasibility

Autor: Alissa Brauneck, Louisa Schmalhorst, Stefan Weiss, Linda Baumbach, Uwe Völker, David Ellinghaus, Jan Baumbach, Gabriele Buchholtz

Publikováno v: Genome Biology, Vol 25, Iss 1, Pp 1-18 (2024)

Abstract Genomic data holds huge potential for medical progress but requires strict safety measures due to its sensitive nature to comply with data protection laws. This conflict is especially pronounced in genome-wide association studies (GWAS) whic

Externí odkaz: https://doaj.org/article/02cc3e4eaf884272bb38bfbc66c04ad2

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Akademický článek

Author Correction: Legal aspects of privacy-enhancing technologies in genome-wide association studies and their impact on performance and feasibility

Autor: Alissa Brauneck, Louisa Schmalhorst, Stefan Weiss, Linda Baumbach, Uwe Völker, David Ellinghaus, Jan Baumbach, Gabriele Buchholtz

Publikováno v: Genome Biology, Vol 25, Iss 1, Pp 1-1 (2024)

Externí odkaz: https://doaj.org/article/05c7fe3008924be3a31d211f6f8c518c

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Akademický článek

Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review

Autor: Alissa Brauneck, Louisa Schmalhorst, Mohammad Mahdi Kazemi Majdabadi, Mohammad Bakhtiari, Uwe Völker, Jan Baumbach, Linda Baumbach, Gabriele Buchholtz

Publikováno v: Journal of Medical Internet Research, Vol 25, p e41588 (2023)

BackgroundThe collection, storage, and analysis of large data sets are relevant in many sectors. Especially in the medical field, the processing of patient data promises great progress in personalized health care. However, it is strictly regulated, s

Externí odkaz: https://doaj.org/article/b49d10bc3edf410f917370bad5cddf09

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Akademický článek

Privacy-aware multi-institutional time-to-event studies.

Autor: Julian Späth, Julian Matschinske, Frederick K Kamanu, Sabina A Murphy, Olga Zolotareva, Mohammad Bakhtiari, Elliott M Antman, Joseph Loscalzo, Alissa Brauneck, Louisa Schmalhorst, Gabriele Buchholtz, Jan Baumbach

Publikováno v: PLOS Digital Health, Vol 1, Iss 9, p e0000101 (2022)

Clinical time-to-event studies are dependent on large sample sizes, often not available at a single institution. However, this is countered by the fact that, particularly in the medical field, individual institutions are often legally unable to share

Externí odkaz: https://doaj.org/article/aad0103cec2e421b9d7eb9303c67f0ef

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Federated machine learning in data-protection-compliant research

Autor: Alissa Brauneck, Louisa Schmalhorst, Mohammad Mahdi Kazemi Majdabadi, Mohammad Bakhtiari, Uwe Völker, Christina Caroline Saak, Jan Baumbach, Linda Baumbach, Gabriele Buchholtz

Publikováno v: Nature Machine Intelligence. 5:2-4

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7f84e01f3bd0cb53f7a8aedd2631d141
https://doi.org/10.1038/s42256-022-00601-5

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Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review (Preprint)

Autor: Alissa Brauneck, Louisa Schmalhorst, Mohammad Mahdi Kazemi Majdabadi, Mohammad Bakhtiari, Uwe Völker, Jan Baumbach, Linda Baumbach, Gabriele Buchholtz

BACKGROUND The collection, storage, and analysis of large data sets are relevant in many sectors. Especially in the medical field, the processing of patient data promises great progress in personalized health care. However, it is strictly regulated,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::eed787939419932231f3f052f5bc44fc
https://doi.org/10.2196/preprints.41588