Using LLMs and ontologies to extract causal relationships from medical abstracts

The substantiation of the causal relationships behind its development is very important in identifying possible interventions and early treatment. Knowledge Graphs (KG) play a crucial role in the medical research domain by organizing data into interconnected structures that represent relationships between entities such as disease, treatments, and progressions. This paper shows a complete workflow that demonstrates the extraction of causal relationships from medical abstracts using a fine-tuned GPT-based model and the integration of these relationships into a KG.