Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context

Understand

Language Models (LMs) have performed well on biomedical natural language processing applications.

• In this study, we conducted some experiments to use prompt methods to extract knowledge from LMs as new knowledge Bases (LMs as KBs).
• However, prompting can only be used as a low bound for knowledge extraction, and perform particularly poorly on biomedical domain KBs.
• In order to make LMs as KBs more in line with the actual application scenarios of the biomedical domain, we specifically add EHR notes as context to the prompt to improve the low bound in the biomedical domain.