Large Language Models for Automatic Deidentification of Electronic Health Record Notes International Workshop, IW-DMRN 2024, Kaohsiung, Taiwan, January 15, 2024, Revised Selected Papers /
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 m...
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| Altri autori: | , , |
| Natura: | Elettronico eBook |
| Lingua: | inglese |
| Pubblicazione: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2025.
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| Edizione: | 1st ed. 2025. |
| Serie: | Communications in Computer and Information Science,
2148 |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/978-981-97-7966-6 |
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Sommario:
- -- 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.