Keyword-optimized Template Insertion for Clinical Information Extraction
  via Prompt-based Learning
Eugenia Alleva Isotta Landi Leslee J Shaw Erwin Böttinger Thomas J Fuchs Ipek Ensari
Clinical note classification is a common clinical NLP task. However,
annotated data-sets are scarse. Prompt-based learning has recently emerged as
an effective method to adapt pre-trained models for text classification using
only few training examples. A critical component of prompt design is the
definition of the template (i.e. prompt text). The effect of template position,
however, has been insufficiently investigated. This seems particularly
important in the clinical setting, where task-relevant information is usually
sparse in clinical notes. In this study we develop a keyword-optimized template
insertion method (KOTI) and show how optimizing position can improve
performance on several clinical tasks in a zero-shot and few-shot training
setting.