Published on in Vol 4, No 5 (2020): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14064, first published .
Privacy-Preserving Deep Learning for the Detection of Protected Health Information in Real-World Data: Comparative Evaluation

Privacy-Preserving Deep Learning for the Detection of Protected Health Information in Real-World Data: Comparative Evaluation

Privacy-Preserving Deep Learning for the Detection of Protected Health Information in Real-World Data: Comparative Evaluation

Authors of this article:

Sven Festag1, 2 Author Orcid Image ;   Cord Spreckelsen1, 2 Author Orcid Image

Journals

  1. Ahsan M, Gupta K, Nag A, Poudyal S, Kouzani A, Mahmud M. Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review. IEEE Access 2020;8:180799 View
  2. Liu J, Goetz J, Sen S, Tewari A. Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data. JMIR mHealth and uHealth 2021;9(3):e23728 View
  3. Wang P, Li Y, Yang L, Li S, Li L, Zhao Z, Long S, Wang F, Wang H, Li Y, Wang C. An Efficient Method for Deidentifying Protected Health Information in Chinese Electronic Health Records: Algorithm Development and Validation. JMIR Medical Informatics 2022;10(8):e38154 View
  4. Xu J, Xi X, Chen J, Sheng V, Ma J, Cui Z. A Survey of Deep Learning for Electronic Health Records. Applied Sciences 2022;12(22):11709 View

Books/Policy Documents

  1. Guerra-Manzanares A, Lopez L, Maniatakos M, Shamout F. Trustworthy Machine Learning for Healthcare. View