English

Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data

Computation and Language 2024-03-29 v1
Authors: Shan Chen , Jack Gallifant , Marco Guevara , Yanjun Gao , Majid Afshar , Timothy Miller , Dmitriy Dligach , Danielle S. Bitterman
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Abstract

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.

Keywords

text generation natural language processing clinical natural language processing

Cite

@article{arxiv.2403.19511,
  title  = {Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data},
  author = {Shan Chen and Jack Gallifant and Marco Guevara and Yanjun Gao and Majid Afshar and Timothy Miller and Dmitriy Dligach and Danielle S. Bitterman},
  journal= {arXiv preprint arXiv:2403.19511},
  year   = {2024}
}

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R2 v1 2026-06-28T15:37:16.639Z