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
4. Silva R, Pazin-Filho A. Anonimização de textos médicos com processamento de linguagem natural. Journal of Health Informatics 2025;17:1227 View
5. Alkalbani A, Alrawahi A, Salah A, Haghighi V, Zhang Y, Alkindi S, Sheng Q. A Systematic Review of Large Language Models in Medical Specialties: Applications, Challenges and Future Directions. Information 2025;16(6):489 View
6. Dai H, Mir T, Chen C, Chen C, Yang H, Lee C, Chou Y, Teng Y, Gupta S, Panchal O, Nadar D, Liao W, Lin Y, Zhao Z, Tsai R, Chang Y, Jonnagaddala J. Leveraging large language models for the deidentification and temporal normalization of sensitive health information in electronic health records. npj Digital Medicine 2025;8(1) View
7. Hossain B, Preum S, Rabbi M, Ara R, Ali M. Extracting Symptoms of Complex Conditions From Online Discourse (Subreddit to Symptomatology): Lexicon-Based Approach. JMIR Medical Informatics 2025;13:e70940 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