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

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

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 bi...

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Bibliographic Details
Main Authors: Johanna Eicher, Raffael Bild, Helmut Spengler, Klaus A. Kuhn, Fabian Prasser
Format: Article
Language:English
Published: BMC 2020-02-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Biomedical data
Prediction models
Machine learning
Classification
Privacy protection
Data anonymization
Online Access:https://doi.org/10.1186/s12911-020-1041-3

Internet

https://doi.org/10.1186/s12911-020-1041-3

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