Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48145, first published .
OpenDeID Pipeline for Unstructured Electronic Health Record Text Notes Based on Rules and Transformers: Deidentification Algorithm Development and Validation Study

OpenDeID Pipeline for Unstructured Electronic Health Record Text Notes Based on Rules and Transformers: Deidentification Algorithm Development and Validation Study

OpenDeID Pipeline for Unstructured Electronic Health Record Text Notes Based on Rules and Transformers: Deidentification Algorithm Development and Validation Study

Journals

  1. Cheng J. Applications of Large Language Models in Pathology. Bioengineering 2024;11(4):342 View
  2. Dorémus O, Russon D, Contrand B, Guerra-Adames A, Avalos-Fernandez M, Gil-Jardiné C, Lagarde E. Harnessing Moderate-Sized Language Models for Reliable Patient Data Deidentification in Emergency Department Records: Algorithm Development, Validation, and Implementation Study. JMIR AI 2025;4:e57828 View
  3. Jonnagaddala J, Wong Z. Privacy preserving strategies for electronic health records in the era of large language models. npj Digital Medicine 2025;8(1) View

Books/Policy Documents

  1. Li Z, Zheng H, Mao K, Wei Z. Large Language Models for Automatic Deidentification of Electronic Health Record Notes. View
  2. Mir T, Yang H, Chou Y, Teng Y, Liao W, Lin Y, Gupta S, Panchal O, Jonnagaddala J, Chen C, Dai H. Large Language Models for Automatic Deidentification of Electronic Health Record Notes. View
  3. Huang C, Rianto B, Sun J, Fu Z, Lee C. Large Language Models for Automatic Deidentification of Electronic Health Record Notes. View
  4. Huang Y, Peng T, Lin H, Sy E, Chang Y. Large Language Models for Automatic Deidentification of Electronic Health Record Notes. View
  5. Chao C, Lin C. Large Language Models for Automatic Deidentification of Electronic Health Record Notes. View
  6. Huang S, Cheng H, Li Z. Large Language Models for Automatic Deidentification of Electronic Health Record Notes. View
  7. Cho Y, Yang Y, Liu Y, Tsao T, Lee M. Large Language Models for Automatic Deidentification of Electronic Health Record Notes. View
  8. Huang T, Shih J, Hsieh Y, Feng H. Large Language Models for Automatic Deidentification of Electronic Health Record Notes. View
  9. Huang M, Mau B, Lin J, Chen Y. Large Language Models for Automatic Deidentification of Electronic Health Record Notes. View
  10. Chiu P, Hou B, Chen Y, Huang S. Large Language Models for Automatic Deidentification of Electronic Health Record Notes. View
  11. Gupta S, Alla N, Panchal O, Witowski J, Jonnagaddala J. Large Language Models for Automatic Deidentification of Electronic Health Record Notes. View
  12. Huang P, Liu T. Large Language Models for Automatic Deidentification of Electronic Health Record Notes. View
  13. Zhao Z, Chou P, Hussain Mir T, Dai H. Large Language Models for Automatic Deidentification of Electronic Health Record Notes. View
  14. Ru Z, Panchal O, Chen C, Jonnagaddala J, Dai H, Hsueh S. Large Language Models for Automatic Deidentification of Electronic Health Record Notes. View

Conference Proceedings

  1. Jain V, Singh I, Syed M, Mondal S, Ranjan Palai D. 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). Enhancing Educational Interactions: A Comprehensive Review of AI Chatbots in Learning Environments View