Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning
<|reference_start|>Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning: The progress in natural language processing (NLP) using large language models (LLMs) has greatly improved patient information extraction from clinical narratives. However, most methods based on the fine-tuning strategy have limited transfer learning ability for cross-domain applications. This study proposed a novel approach that employs a soft prompt-based learning architecture, which introduces trainable prompts to guide LLMs toward desired outputs. We examined two types of LLM architectures, including encoder-only GatorTron and decoder-only GatorTronGPT, and evaluated their performance for the extraction of social determinants of health (SDoH) using a cross-institution dataset from the 2022 n2c2 challenge and a cross-disease dataset from the University of Florida (UF) Health. The results show that decoder-only LLMs with prompt tuning achieved better performance in cross-domain applications. GatorTronGPT achieved the best F1 scores for both datasets, outperforming traditional fine-tuned GatorTron by 8.9% and 21.8% in a cross-institution setting, and 5.5% and 14.5% in a cross-disease setting.<|reference_end|>
@article{peng2024improving,
    title={Improving Generalizability of Extracting Social Determinants of Health
  Using Large Language Models through Prompt-tuning},
    author={Cheng Peng, Zehao Yu, Kaleb E Smith, Wei-Hsuan Lo-Ciganic, Jiang Bian,
  Yonghui Wu},
    journal={arXiv preprint arXiv:2403.12374},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12374},
    primaryClass={cs.CL}
}