Springer Large Language Models For Automatic Deidentification Of Sensitive Health Information In Clinical Speech
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Large Language Models for Automatic Deidentification of Sensitive Health Information in Clinical Speech2025 International workshop on Deidentification of Electronic Medical Record Notes (2025 IW-DMRN), Taipei, Taiwan, August 10, 2025, Revised Selected Papers\nAuthor(s): Jitendra Jonnagaddala, Hong-Jie Dai, Ching-Tai Chen, Yung-Chung Chang, Hui-Hsien Feng\nFormat: Paperback\nPublisher: Springer Verlag, Singapore, Singapore\nImprint: Springer Nature\nISBN-13: 9789819222810, 978-9819222810\nSynopsis\nThis 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.\nThe 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 patientclinician communications.
Springer Large Language Models For Automatic Deidentification Of Sensitive Health Information In Clinical Speech
Large Language Models for Automatic Deidentification of Sensitive Health Information in Clinical Speech2025 International workshop on Deidentification of Electronic Medical Record Notes (2025 IW-DMRN), Taipei, Taiwan, August 10, 2025, Revised Selected Papers\nAuthor(s): Jitendra Jonnagaddala, Hong-Jie Dai, Ching-Tai Chen, Yung-Chung Chang, Hui-Hsien Feng\nFormat: Paperback\nPublisher: Springer Verlag, Singapore, Singapore\nImprint: Springer Nature\nISBN-13: 9789819222810, 978-9819222810\nSynopsis\nThis 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.\nThe 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 patientclinician communications.
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