Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records

Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records

A major hurdle in the development of natural language processing (NLP) methods for Electronic Health Records (EHRs) is the lack of large, annotated datasets. Privacy concerns prevent the distribution of EHRs, and the annotation of data is known to be costly and cumbersome. Synthetic data presents a...

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Detaylı Bibliyografya
Asıl Yazarlar: Claudia Alessandra Libbi, Jan Trienes, Dolf Trieschnigg, Christin Seifert
Materyal Türü: Makale
Dil:English
Baskı/Yayın Bilgisi: MDPI AG 2021-05-01
Seri Bilgileri:Future Internet
Konular:
natural language processing
medical records
privacy protection
synthetic text
generative language models
named-entity recognition
Online Erişim:https://www.mdpi.com/1999-5903/13/5/136

Internet

https://www.mdpi.com/1999-5903/13/5/136

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