2022/03/09 by Sonish Sivarajkumar, Sivarajkumar, Sonish, Yanshan Wang +1 · 3 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial Intelligence (cs.AI) #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning in Healthcare #Topic Modeling
paper · doi:10.48550/arxiv.2203.05061
openalex publication_date 2022/03/09 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/29
Developing clinical natural language systems based on machine learning and deep learning is dependent on the availability of large-scale annotated clinical text datasets, most of which are time-consuming to create and not publicly available. The lack of such annotated datasets is the biggest bottleneck for the development of clinical 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 in NLP where we define task-based templates for different tasks. In this study, 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-training-data setting. Our experiments show that HealthPrompt could effectively capture the context of clinical texts and perform well for clinical NLP tasks without any training data.