Del i serien Communications in Computer and Information Science

Large Language Models for Automatic Deidentification of Electronic Health Record Notes

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

Häftad, Engelska, 2025

AvJitendra Jonnagaddala,Hong-Jie Dai,Ching-Tai Chen

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This volume constitutes the refereed proceedings of the International Workshop on Deidentification of Electronic Health Record Notes, IW-DMRN 2024, held on January 15, 2024, in Kaohsiung, Taiwan. The 15 full papers were carefully reviewed and selected from 30 submissions.

Produktinformation

Utgivningsdatum2025-01-26
Mått155 x 235 x 13 mm
Vikt353 g
FormatHäftad
SpråkEngelska
SerieCommunications in Computer and Information Science
Antal sidor214
FörlagSpringer Verlag, Singapore
ISBN9789819779659

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Systemvetenskap och AI inom Data och IT
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.- 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.