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Deep learning algorithms are dependent on the availability of large-scaleannotated clinical text datasets. The lack of such publicly available datasetsis the biggest bottleneck for the development of clinical Natural LanguageProcessing(NLP) systems. Zero-Shot Learning(ZSL) refers to the use of deeplearning models to classify instances from new classes of which no trainingdata have been seen before. Prompt-based learning is an emerging ZSL techniquewhere we define task-based templates for NLP tasks. We developed a novelprompt-based clinical NLP framework called HealthPrompt and applied theparadigm of prompt-based learning on clinical texts. In this technique, ratherthan fine-tuning a Pre-trained Language Model(PLM), the task definitions aretuned by defining a prompt template. We performed an in-depth analysis ofHealthPrompt on six different PLMs in a no-data setting. Our experiments provethat prompts effectively capture the context of clinical texts and performremarkably well without any training data.

HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing