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Distributed Non-Disclosive Validation of Predictive Models by a Modified ROC-GLM

MCML Authors

Daniel Schalk

Dr.

* Former Member

→ Group Bernd Bischl
Statistical Learning and Data Science

Raphael Rehms

Dr.

Thomas Bayes Fellow

→ Group Daniel Rückert
Artificial Intelligence in Healthcare and Medicine

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Abstract

Distributed statistical analyses provide a promising approach for privacy protection when analyzing data distributed over several databases. Instead of directly operating on data, the analyst receives anonymous summary statistics, which are combined into an aggregated result. Further, in discrimination model (prognosis, diagnosis, etc.) development, it is key to evaluate a trained model w.r.t. to its prognostic or predictive performance on new independent data. For binary classification, quantifying discrimination uses the receiver operating characteristics (ROC) and its area under the curve (AUC) as aggregation measure. We are interested to calculate both as well as basic indicators of calibration-in-the-large for a binary classification task using a distributed and privacy-preserving approach...

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