000K  utf8
1100  2021$c2021-05-20
1500  eng
2050  urn:nbn:de:hbz:464-20210813-101610-6
2051  10.3390/fi13050136
3000  Libbi, Claudia Alessandra
3010  Seifert, Christin
3010  Trienes, Jan
3010  Trieschnigg, Dolf
4000  Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records  [Libbi, Claudia Alessandra]
4209  A major hurdle in the development of natural language processing (NLP) methods for Electronic Health Records (EHRs) is the lack of large, annotated datasets. Privacy concerns prevent the distribution of EHRs, and the annotation of data is known to be costly and cumbersome. Synthetic data presents a promising solution to the privacy concern, if synthetic data has comparable utility to real data and if it preserves the privacy of patients. However, the generation of synthetic text alone is not useful for NLP because of the lack of annotations. In this work, we propose the use of neural language models (LSTM and GPT-2) for generating artificial EHR text jointly with annotations for named-entity recognition. Our experiments show that artificial documents can be used to train a supervised named-entity recognition model for de-identification, which outperforms a state-of-the-art rule-based baseline. Moreover, we show that combining real data with synthetic data improves the recall of the method, without manual annotation effort. We conduct a user study to gain insights on the privacy of artificial text. We highlight privacy risks associated with language models to inform future research on privacy-preserving automated text generation and metrics for evaluating privacy-preservation during text generation.
4950  https://doi.org/10.3390/fi13050136$xR$3Volltext$534
4950  https://nbn-resolving.org/urn:nbn:de:hbz:464-20210813-101610-6$xR$3Volltext$534
4961  https://duepublico2.uni-due.de/receive/duepublico_mods_00074632
5051  610
5550  generative language models
5550  medical records
5550  named-entity recognition
5550  natural language generation
5550  natural language processing
5550  privacy protection
5550  synthetic text