Work

From Patient Consultations to Graphs: Leveraging LLMs for Patient Journey Knowledge Graph Construction

Year: 2025

Type: conference-paper

Abstract: The shift toward patient-centric healthcare requires understanding comprehensive patient journeys. Current healthcare data systems often fail to provide holistic representations, hindering coordinated care. Patient Journey Knowledge Graphs (PJKGs) solve this by integrating diverse patient information into unified, structured formats. This paper presents a methodology for constructing PJKGs using Large Language Models (LLMs) to process both clinical documentation and patient-provider conversations. These graphs capture temporal and causal relationships between clinical events, enabling advanced reasoning and personalized insights. Our evaluation of four LLMs (Claude 3.5, Mistral, Llama 3.1, ChatGPT4o) shows all achieved perfect structural compliance but varied in medical entity processing, computational efficiency, and semantic accuracy. This work advances patient-centric healthcare through actionable knowledge graphs (KGs) that enhance care coordination and outcome prediction.

Source: IEEE Conference on Artificial Intelligence (CAI)

DOI: 10.1109/cai64502.2025.00075

Authors: Hassan S. Al Khatib, Sudip Mittal, Shahram Rahimi, Nina Marhamati, Sean Bozorgzad

Institutions: Mississippi State University, University of Alabama

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Nursing Research