Fold-stratified cross-validation for unbiased and privacy-preserving federated learning.

ABSTRACT:

Objective

We introduce fold-stratified cross-validation, a validation methodology that is compatible with privacy-preserving federated learning and that prevents data leakage caused by duplicates of electronic health records (EHRs).

Materials and methods

Fold-stratified cross-validation complements cross-validation with an initial stratification of EHRs in folds containing patients with similar characteristics, thus ensuring that duplicates of a record are jointly present either in training or in validation folds. Monte Carlo simulations are performed to investigate the properties of fold-stratified cross-validation in the case of a model data analysis using both synthetic data and MIMIC-III (Medical Information Mart for Intensive Care-III) medical records.

Results

In

SUBMITTER: Bey R 

PROVIDER: S-EPMC7647321 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature