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

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arxiv 2403.19511 v1 pith:V4NMFC7B submitted 2024-03-28 cs.CL

Shan Chen , Jack Gallifant , Marco Guevara , Yanjun Gao , Majid Afshar , Timothy Miller , Dmitriy Dligach , Danielle S. Bitterman This is my paper · ORCID
classification cs.CL
keywords clinicaldatalanguagemodelsperformancesyntheticadvancedapplications
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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.

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  1. Thousand Voices of Trauma: A Large-Scale Synthetic Dataset for Modeling Prolonged Exposure Therapy Conversations

    cs.CY 2025-04 conditional novelty 6.0 of 10

    A synthetic dataset of 3,000 prolonged exposure therapy conversations for PTSD, with an emotional trajectory benchmark and expert evaluation, is introduced.

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