HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing
This addresses the problem of developing clinical NLP systems for healthcare professionals by providing a novel zero-shot approach, though it is incremental as it adapts existing prompt-based methods to a specific domain.
The paper tackled the bottleneck of limited annotated clinical text data by introducing HealthPrompt, a zero-shot learning framework using prompt-based learning, which achieved remarkable performance without any training data across six pre-trained language models.
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.