Large Language Models for Automatic Deidentification of Electronic Health Record Notes International Workshop, IW-DMRN 2024, Kaohsiung, Taiwan, January 15, 2024, Revised Selected Papers

.- Deidentification And Temporal Normalization of The Electronic Health Record Notes Using Large Language Models: The 2023 SREDH/AI-Cup Competition for Deidentification of Sensitive Health Information.

.- Enhancing Automated De-identification of PathologyText Notes Using Pre-Trained Language Models.

.- A Comparative Study of GPT3.5 Fine Tuning and Rule-Based Approaches for De-identification and Normalization of Sensitive Health Information in Electronic Medical Record Notes.

.- Advancing Sensitive Health Data Recognition and Normalization through Large Language Model Driven Data Augmentation.

.- Privacy Protection and Standardization of Electronic Medical Records Using Large Language Model.

.- Applying Language Models for Recognizing and Normalizing Sensitive Information from Electronic Health Records Text Notes.

.- Enhancing SHI Extraction and Time Normalization in Healthcare Records Using LLMs and Dual- Model Voting.

.- Evaluation of OpenDeID Pipeline in the 2023 SREDH/AI-Cup Competition for Deidentification of Sensitive Health Information.

.- Sensitive Health Information Extraction from EMR Text Notes: A Rule-Based NER Approach Using Linguistic Contextual Analysis.

.- A Hybrid Approach to the Recognition of Sensitive Health Information: LLM and Regular Expressions.

.- Patient Privacy Information Retrieval with Longformer and CRF, Followed by Rule-Based Time Information Normalization: A Dual-Approach Study.

.- A Deep Dive into the Application of Pythia for Enhancing Medical Information De-identification in the AI CUP 2023.

.- Utilizing Large Language Models for Privacy Protection and Advancing Medical Digitization.

.- Comprehensive Evaluation of Pythia Model Efficiency in De-identification and Normalization for Enhanced Medical Data Management.

.- A Two-stage Fine-tuning Procedure to Improve the Performance of Language Models in Sensitive Health Information Recognition and Normalization Tasks.