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

Electronic Health Records (1276) Ethics, Privacy, and Legal Issues (593) Machine Learning (1984) Natural Language Processing (873) Policy (74) Secondary Use of Clinical Data for Research and Surveillance (456) Big Data (305)

Published on in Vol 10, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38154, first published .
An Efficient Method for Deidentifying Protected Health Information in Chinese Electronic Health Records: Algorithm Development and Validation

An Efficient Method for Deidentifying Protected Health Information in Chinese Electronic Health Records: Algorithm Development and Validation

An Efficient Method for Deidentifying Protected Health Information in Chinese Electronic Health Records: Algorithm Development and Validation

Authors of this article:

Peng Wang1 Author Orcid Image ;   Yong Li2 Author Orcid Image ;   Liang Yang3 Author Orcid Image ;   Simin Li3 Author Orcid Image ;   Linfeng Li3 Author Orcid Image ;   Zehan Zhao4 Author Orcid Image ;   Shaopei Long2 Author Orcid Image ;   Fei Wang5 Author Orcid Image ;   Hongqian Wang5 Author Orcid Image ;   Ying Li5 Author Orcid Image ;   Chengliang Wang1 Author Orcid Image
Article Authors Cited by (5) Tweetations (2) Metrics

Journals

  1. Kovačević A, Bašaragin B, Milošević N, Nenadić G. De-identification of clinical free text using natural language processing: A systematic review of current approaches. Artificial Intelligence in Medicine 2024;151:102845 View
  2. Huang Y, Chen Y, Lin C, Chiu H, Chang Y. A nursing note-aware deep neural network for predicting mortality risk after hospital discharge. International Journal of Nursing Studies 2024;156:104797 View
  3. Xu K, Song Y, Ma J. Identifying protected health information by transformers-based deep learning approach in Chinese medical text. Health Informatics Journal 2025;31(1) View
  4. An J, Kim J, Sunwoo L, Baek H, Yoo S, Lee S. De-identification of clinical notes with pseudo-labeling using regular expression rules and pre-trained BERT. BMC Medical Informatics and Decision Making 2025;25(1) View

Conference Proceedings

  1. Zou Y, Zhang P, Ling Y, Lv D, Li Z, Lu S, Zhang G. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). A domain adaptive pre-training language model for sentence classification of Chinese electronic medical record View