Article... IEEE Conference on Artificial Intelligence2025
From Patient Consultations to Graphs: Leveraging LLMs for Patient Journey Knowledge Graph Construction.
Article in ... IEEE Conference on Artificial Intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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5 authors.
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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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.