Work

Using LLMs and ontologies to extract causal relationships from medical abstracts

Year: 2024

Type: conference-paper

Abstract: 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.

Source: Procedia Computer Science

DOI: 10.1016/j.procs.2024.10.219

Authors: Alexandru Lecu, Adrian Groza, Lezan Hawizy

Institutions: Technical University of Cluj-Napoca, Digital Science (United Kingdom)

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: diamond

Funder: Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii