Large Language Models for Biomedical Causal Graph Construction

Automatic causal graph construction is of high importance in medical research. They have many applications, such as clinical trial criteria design, where identification of confounding variables is a crucial step. The quality bar for clinical applications is high, and the lack of public corpora is a barrier for such studies. Large language models (LLMs) have demonstrated impressive capabilities in natural language processing and understanding, so applying such models in clinical settings is an attractive direction, especially in clinical applications with complex relations between entities, such as diseases, symptoms and treatments. Whereas, relation extraction has already been studied using LLMs, here we present an end-to-end machine learning solution of causal relationship analysis between aforementioned entities using EMR notes. Additionally, in comparison to other studies, we demonstrate extensive evaluation of the method.