TY  - JOUR
AU  - Festag, Sven
AU  - Spreckelsen, Cord
PY  - 2020
DA  - 2020/5/5
TI  - Privacy-Preserving Deep Learning for the Detection of Protected Health Information in Real-World Data: Comparative Evaluation
JO  - JMIR Form Res
SP  - e14064
VL  - 4
IS  - 5
KW  - privacy-preserving protocols
KW  - neural networks
KW  - health informatics
KW  - distributed machine learning
AB  - Background: Collaborative privacy-preserving training methods allow for the integration of locally stored private data sets into machine learning approaches while ensuring confidentiality and nondisclosure. Objective: In this work we assess the performance of a state-of-the-art neural network approach for the detection of protected health information in texts trained in a collaborative privacy-preserving way. Methods: The training adopts distributed selective stochastic gradient descent (ie, it works by exchanging local learning results achieved on private data sets). Five networks were trained on separated real-world clinical data sets by using the privacy-protecting protocol. In total, the data sets contain 1304 real longitudinal patient records for 296 patients. Results: These networks reached a mean F1 value of 0.955. The gold standard centralized training that is based on the union of all sets and does not take data security into consideration reaches a final value of 0.962. Conclusions: Using real-world clinical data, our study shows that detection of protected health information can be secured by collaborative privacy-preserving training. In general, the approach shows the feasibility of deep learning on distributed and confidential clinical data while ensuring data protection. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2020/5/e14064
UR  - https://doi.org/10.2196/14064
UR  - http://www.ncbi.nlm.nih.gov/pubmed/32369025
DO  - 10.2196/14064
ID  - info:doi/10.2196/14064
ER  -