Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data
  Generation with Large Language Models
Ran Xu Hejie Cui Yue Yu Xuan Kan Wenqi Shi Yuchen Zhuang Wei Jin Joyce Ho Carl Yang
Clinical natural language processing requires methods that can address
domain-specific challenges, such as complex medical terminology and clinical
contexts. Recently, large language models (LLMs) have shown promise in this
domain. Yet, their direct deployment can lead to privacy issues and are
constrained by resources. To address this challenge, we delve into synthetic
clinical text generation using LLMs for clinical NLP tasks. We propose an
innovative, resource-efficient approach, ClinGen, which infuses knowledge into
the process. Our model involves clinical knowledge extraction and
context-informed LLM prompting. Both clinical topics and writing styles are
drawn from external domain-specific knowledge graphs and LLMs to guide data
generation. Our extensive empirical study across 7 clinical NLP tasks and 16
datasets reveals that ClinGen consistently enhances performance across various
tasks, effectively aligning the distribution of real datasets and significantly
enriching the diversity of generated training instances. We will publish our
code and all the generated data in \url{https://github.com/ritaranx/ClinGen}.