Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data
Fuente:
arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917624838881280 |
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| author | Chen, Shan Gallifant, Jack Guevara, Marco Gao, Yanjun Afshar, Majid Miller, Timothy Dligach, Dmitriy Bitterman, Danielle S. |
| author_facet | Chen, Shan Gallifant, Jack Guevara, Marco Gao, Yanjun Afshar, Majid Miller, Timothy Dligach, Dmitriy Bitterman, Danielle S. |
| contents | Generative models have been showing potential for producing data in mass. This study explores the enhancement of clinical natural language processing performance by utilizing synthetic data generated from advanced language models. Promising results show feasible applications in such a high-stakes domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19511 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data Chen, Shan Gallifant, Jack Guevara, Marco Gao, Yanjun Afshar, Majid Miller, Timothy Dligach, Dmitriy Bitterman, Danielle S. Computation and Language Generative models have been showing potential for producing data in mass. This study explores the enhancement of clinical natural language processing performance by utilizing synthetic data generated from advanced language models. Promising results show feasible applications in such a high-stakes domain. |
| title | Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2403.19511 |