This talk is part of the ISP AI Forum series.

Abstract: 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 talk, I’ll go through the challenges of clinical NLP development and how the novel HealthPrompt framework can resolve some of these challenges.

Bio: Sonish Sivarajkumar is a PhD candidate in the Intelligent Systems Program. His research interests include deep learning, interpretable AI, biomedical informatics, and multi-omics.

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