東華大學圖書館 |

Large language models for automatic deidentification of electronic health record notes = International Workshop, IW-DMRN 2024, Kaohsiung, Taiwan, January 15, 2024 : revised selected papers /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Large language models for automatic deidentification of electronic health record notes/ edited by Jitendra Jonnagaddala, Hong-Jie Dai, Ching-Tai Chen.
Reminder of title:	International Workshop, IW-DMRN 2024, Kaohsiung, Taiwan, January 15, 2024 : revised selected papers /
remainder title:	IW-DMRN 2024
other author:	Jonnagaddala, Jitendra.
corporate name:	International Workshop on Deidentification of Electronic Health Record Notes
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xii, 214 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Medical records - Congresses. - Data processing -
Online resource:	https://doi.org/10.1007/978-981-97-7966-6
ISBN:	9789819779666

Large language models for automatic deidentification of electronic health record notes = International Workshop, IW-DMRN 2024, Kaohsiung, Taiwan, January 15, 2024 : revised selected papers /
Large language models for automatic deidentification of electronic health record notesInternational Workshop, IW-DMRN 2024, Kaohsiung, Taiwan, January 15, 2024 : revised selected papers /[electronic resource] :IW-DMRN 2024edited by Jitendra Jonnagaddala, Hong-Jie Dai, Ching-Tai Chen. - Singapore :Springer Nature Singapore :2025. - xii, 214 p. :ill. (some col.), digital ;24 cm. - Communications in computer and information science,21481865-0937 ;. - Communications in computer and information science ;2148..

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.

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. The conference focuses on medical data analysis, enhancing medication safety, and optimizing medical care efficiency.

ISBN: 9789819779666

Standard No.: 10.1007/978-981-97-7966-6doiSubjects--Topical Terms:

3781038
Medical records
--Data processing--Congresses.

LC Class. No.: R864

Dewey Class. No.: 651.504261

Large language models for automatic deidentification of electronic health record notes = International Workshop, IW-DMRN 2024, Kaohsiung, Taiwan, January 15, 2024 : revised selected papers /
LDR:03639nmm a2200373 a 4500 001 2408511
003 DE-He213
005 20250125115233.0
006 m d
007 cr nn 008maaau
008 260204s2025 si s 0 eng d
020 $a 9789819779666 $q (electronic bk.)
020 $a 9789819779659 $q (paper)
024 7 $a 10.1007/978-981-97-7966-6 $2 doi
035 $a 978-981-97-7966-6
040 $a GP $c GP
041 0 $a eng
050 4 $a R864
072 7 $a UR $2 bicssc
072 7 $a UTN $2 bicssc
072 7 $a COM053000 $2 bisacsh
072 7 $a UR $2 thema
072 7 $a UTN $2 thema
082 0 4 $a 651.504261 $2 23
090 $a R864 $b .I61 2024
111 2 $a International Workshop on Deidentification of Electronic Health Record Notes $d (2024 : $c Kaohsiung City, Taiwan) $3 3781033
245 1 0 $a Large language models for automatic deidentification of electronic health record notes $h [electronic resource] : $b International Workshop, IW-DMRN 2024, Kaohsiung, Taiwan, January 15, 2024 : revised selected papers / $c edited by Jitendra Jonnagaddala, Hong-Jie Dai, Ching-Tai Chen.
246 3 $a IW-DMRN 2024
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a xii, 214 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Communications in computer and information science, $x 1865-0937 ; $v 2148
505 0 $a 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.
520 $a 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. The conference focuses on medical data analysis, enhancing medication safety, and optimizing medical care efficiency.
650 0 $a Medical records $x Data processing $x Security measures $v Congresses. $3 3781038
650 1 4 $a Data and Information Security. $3 3538510
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Natural Language Processing (NLP). $3 3755514
650 2 4 $a Computer Application in Administrative Data Processing. $3 3594379
650 2 4 $a Data Analysis and Big Data. $3 3538537
700 1 $a Jonnagaddala, Jitendra. $3 3781034
700 1 $a Dai, Hong-Jie. $3 3781035
700 1 $a Chen, Ching-Tai. $3 3781036
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Communications in computer and information science ; $v 2148. $3 3781037
856 4 0 $u https://doi.org/10.1007/978-981-97-7966-6
950 $a Computer Science (SpringerNature-11645)