Published on in Vol 22, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18055, first published .
Exploring the Privacy-Preserving Properties of Word Embeddings: Algorithmic Validation Study

Exploring the Privacy-Preserving Properties of Word Embeddings: Algorithmic Validation Study

Exploring the Privacy-Preserving Properties of Word Embeddings: Algorithmic Validation Study

Journals

  1. Sammani A, Bagheri A, van der Heijden P, te Riele A, Baas A, Oosters C, Oberski D, Asselbergs F. Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks. npj Digital Medicine 2021;4(1) View
  2. Oyebode O, Ndulue C, Adib A, Mulchandani D, Suruliraj B, Orji F, Chambers C, Meier S, Orji R. Health, Psychosocial, and Social Issues Emanating From the COVID-19 Pandemic Based on Social Media Comments: Text Mining and Thematic Analysis Approach. JMIR Medical Informatics 2021;9(4):e22734 View
  3. Flamholz Z, Crane-Droesch A, Ungar L, Weissman G. Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information. Journal of Biomedical Informatics 2022;125:103971 View
  4. Ma B, Lai E, Yan W, Wu J. A privacy-preserving word embedding text classification model based on privacy boundary constructed by deep belief network. Multimedia Tools and Applications 2023;83(10):30181 View