Feasibility of Using Zero-Shot Learning in Transformer-Based Natural Language Processing Algorithm for Key Information Extraction from Head and Neck Tumor Board Notes.

We demonstrated that a transformer-based extractive question-answering NLP algorithm can be successfully used for extracting information from head and neck tumor board notes with zero-shot learning. Furthermore, our results highlight the significance of prompt engineering for applying NLP for this task. Future work on finetuning these algorithms to oncology-specific texts can potentially enhance algorithm performance for more difficult tasks.