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Title:

A comprehensive tool for creating and evaluating privacy-preserving biomedical prediction models.

Document type:
Article; Journal Article
Author(s):
Eicher, Johanna; Bild, Raffael; Spengler, Helmut; Kuhn, Klaus A; Prasser, Fabian
Abstract:
BACKGROUND: Modern data driven medical research promises to provide new insights into the development and course of disease and to enable novel methods of clinical decision support. To realize this, machine learning models can be trained to make predictions from clinical, paraclinical and biomolecular data. In this process, privacy protection and regulatory requirements need careful consideration, as the resulting models may leak sensitive personal information. To counter this threat, a wide ran...     »
Journal title abbreviation:
BMC Med Inform Decis Mak
Year:
2020
Journal volume:
20
Journal issue:
1
Fulltext / DOI:
doi:10.1186/s12911-020-1041-3
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/32046701
Print-ISSN:
1472-6947
TUM Institution:
Institut für Medizinische Statistik und Epidemiologie
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