Deep learning algorithms are dependent on the availability of large-scale
annotated clinical text datasets. The lack of such publicly available datasets
is the biggest bottleneck for the development of clinical Natural Language
Processing(NLP) systems. Zero-Shot Learning(ZSL) refers to the use of deep
learning models to classify instances from new classes of which no training
data have been seen before. Prompt-based learning is an emerging ZSL technique
where we define task-based templates for NLP tasks. We developed a novel
prompt-based clinical NLP framework called HealthPrompt and applied the
paradigm of prompt-based learning on clinical texts. In this technique, rather
than fine-tuning a Pre-trained Language Model(PLM), the task definitions are
tuned by defining a prompt template. We performed an in-depth analysis of
HealthPrompt on six different PLMs in a no-data setting. Our experiments prove
that prompts effectively capture the context of clinical texts and perform
remarkably well without any training data.

本研究提出了一种基于最新的技术，即零样本学习和提示学习，开发了一种名为 HealthPrompt 的新型基于提示的临床自然语言处理 (NLP) 框架，通过在无数据环境下进行了六个不同的预训练语言模型 (PLM) 的实验，证明了提示能够有效地捕捉临床文本的上下文，并且可以在没有任何训练数据的情况下取得显著的表现。