Large Language Models for Automatic Deidentification of Electronic Health Record Notes

Large Language Models for Automatic Deidentification of Electronic Health Record Notes

International Workshop, IW-DMRN 2024, Kaohsiung, Taiwan, January 15, 2024, Revised Selected Papers

Chen, Ching-Tai; Dai, Hong-Jie; Jonnagaddala, Jitendra

Springer Verlag, Singapore

02/2025

215

Mole

9789819779659

Pré-lançamento - envio 15 a 20 dias após a sua edição

Descrição não disponível.
.- 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.
Data and Information Security;Data mining and knowledge discovery;Natural language processing (NLP);Computer Application in Administrative Data Processing;Data analysis and big data;Artificial Intelligence;Machine Learning;Sensitive Health Information;Electronic Health Record;Natural Language Processing;Large Language Models;Deidentification;temporal information normalization