Large Language Models for Automatic Deidentification of Sensitive Health Information in Clinical Speech
Description
This volume constitutes the refereed proceedings of the 2025 International Workshop on Deidentification of Electronic Health Record Notes, IW-DMRN 2025, held in Taipei, Taiwan, during August 10, 2025.
The 9 full papers were included in this were carefully reviewed and selected from 25 submissions. They focus on the foundational requirement for enabling the safe, scalable, and ethical secondary use of healthcare data. Clinical documentation and patient-clinician communications.
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Content
.- Instruction-Tuned LLMs for Multilingual Medical ASR and Privacy Entity Extraction.
.- Temporal Subword De-identification of Medical Speech for Privacy Protection Leveraging ASR and LLMs.
.- Prompt Engineering and Post-processing for Sensitive Health Information Recognition.
.- Named Entity Recognition in Chinese-English Speech Using Automatic Speech Recognition and Large Language Models.
.- A Two-Stage Generative Framework for Sensitive Health Information Extraction and Temporal Normalization in Medical Records.
.- Recognition of Sensitive Personal Data in Doctor-Patient Speech.
.- Multistage Automatic Speech Recognition- Named Entity Recognition Framework for Privacy Sensitive Information Recognition in Medical Speech Data.
.- Speech De-identification of Chinese, English and Min-nan: Effectiveness of Chinese-based LLM Model and ASR.
.- A Generative Large Language Model-based Approach for Sensitive Data Identification in Medical Speech.