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

作     者：Chen, Shan Gallifant, Jack Guevara, Marco Gao, Yanjun Afshar, Majid Miller, Timothy Dligach, Dmitriy Bitterman, Danielle S. 

作者机构：Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Brigham and Women's Hospital Dana-Farber Cancer Institute BostonMA United States Computational Health Informatics Program Boston Children's Hospital Harvard Medical School BostonMA United States Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Department of Critical Care Guy's & St Thomas' NHS Trust London United Kingdom Department of Medicine University of Wisconsin School of Medicine and Public Health MadisonWI United States Department of Computer Science Loyola University Chicago ChicagoIL United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Natural language processing systems 

摘      要：Brief communication 70 words: 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. © 2024, CC BY-NC-SA.