Published on in Vol 6 , No 2 (2018) :Apr-Jun

Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis

Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis

Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis

Journals

  1. Goecks J, Jalili V, Heiser L, Gray J. How Machine Learning Will Transform Biomedicine. Cell 2020;181(1):92 View
  2. Li Z, Roberts K, Jiang X, Long Q. Distributed learning from multiple EHR databases: Contextual embedding models for medical events. Journal of Biomedical Informatics 2019;92:103138 View
  3. Wang Y, Wen A, Liu S, Hersh W, Bedrick S, Liu H. Test collections for electronic health record-based clinical information retrieval. JAMIA Open 2019;2(3):360 View
  4. Kapa S, Halamka J, Raskar R. Contact Tracing to Manage COVID-19 Spread—Balancing Personal Privacy and Public Health. Mayo Clinic Proceedings 2020;95(7):1320 View
  5. Kim M, Lee J, Ohno-Machado L, Jiang X. Secure and Differentially Private Logistic Regression for Horizontally Distributed Data. IEEE Transactions on Information Forensics and Security 2020;15:695 View
  6. Jia Z, Zeng X, Duan H, Lu X, Li H. A patient-similarity-based model for diagnostic prediction. International Journal of Medical Informatics 2020;135:104073 View
  7. Liu Y, Tian M, Xu C, Zhao L. Neural network feature learning based on image self-encoding. International Journal of Advanced Robotic Systems 2020;17(2):172988142092165 View
  8. Rieke N, Hancox J, Li W, Milletarì F, Roth H, Albarqouni S, Bakas S, Galtier M, Landman B, Maier-Hein K, Ourselin S, Sheller M, Summers R, Trask A, Xu D, Baust M, Cardoso M. The future of digital health with federated learning. npj Digital Medicine 2020;3(1) View
  9. Yin F, Lin Z, Kong Q, Xu Y, Li D, Theodoridis S, Cui S. FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing. IEEE Open Journal of Signal Processing 2020;1:187 View
  10. Park C, Seo S, Kang N, Ko B, Choi B, Park C, Chang D, Kim H, Kim H, Lee H, Jang J, Ye J, Jeon J, Seo J, Kim K, Jung K, Kim N, Paek S, Shin S, Yoo S, Choi Y, Kim Y, Yoon H. Artificial Intelligence in Health Care: Current Applications and Issues. Journal of Korean Medical Science 2020;35(42) View
  11. Si Y, Du J, Li Z, Jiang X, Miller T, Wang F, Jim Zheng W, Roberts K. Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review. Journal of Biomedical Informatics 2021;115:103671 View
  12. Xu J, Glicksberg B, Su C, Walker P, Bian J, Wang F. Federated Learning for Healthcare Informatics. Journal of Healthcare Informatics Research 2021;5(1):1 View
  13. 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
  14. Kirienko M, Sollini M, Ninatti G, Loiacono D, Giacomello E, Gozzi N, Amigoni F, Mainardi L, Lanzi P, Chiti A. Distributed learning: a reliable privacy-preserving strategy to change multicenter collaborations using AI. European Journal of Nuclear Medicine and Molecular Imaging 2021 View
  15. Feki I, Ammar S, Kessentini Y, Muhammad K. Federated learning for COVID-19 screening from Chest X-ray images. Applied Soft Computing 2021;106:107330 View
  16. Lee T, Lee J, Jun C. Bilingual autoencoder-based efficient harmonization of multi-source private data for accurate predictive modeling. Information Sciences 2021;568:403 View
  17. Abdulkareem M, Petersen S. The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype. Frontiers in Artificial Intelligence 2021;4 View
  18. Danilevicz M, Bayer P, Nestor B, Bennamoun M, Edwards D. Resources for image-based high-throughput phenotyping in crops and data sharing challenges. Plant Physiology 2021 View

Books/Policy Documents

  1. Silva S, Altmann A, Gutman B, Lorenzi M. Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning. View