This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2193) Ethics, Privacy, and Legal Issues (456) Machine Learning (1370)

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
Article Authors Cited by (6) Tweetations (1) Metrics

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
  5. Liu C, Jiao Y, Su L, Liu W, Zhang H, Nie S, Gong M. Extraction of Pregnancy and Gestation Information from Electronic Medical Records: Effective Privacy Protection Strategies in a National Healthcare Data Network in China (Preprint). Journal of Medical Internet Research 2023 View

Books/Policy Documents

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