Research on pre-examination and triage framework of large language model based on knowledge graph enhancement

Corresponding author: fangan, fang.an@imicams.ac.cn

DOI: 10.12201/bmr.202608.00014

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  Abstract: Purpose / SignificanceBased on the knowledge graph enhanced large language model technology, a pre-examination and triage framework is constructed to improve the intelligence level of pre-examination and triage and improve the patient ''s experience. Methods / ProcessCombining the knowledge graph and the hospital department directory information, a knowledge graph enhanced large language model pre-examination and triage framework ( KGE-LLM TF ) is proposed to accurately map the patient ''s symptom description to the specific departments of the target hospital, and realize the adaptive docking between the general knowledge graph and the heterogeneous hospital registration system. Results / Conclusion Experiments show that the ACC @ 1 ( Top-1 accuracy rate ) and ACC @ 3 ( Top-3 accuracy rate ) of the framework method reach 84.91 % and 92.45 %, respectively. The accuracy rate and F1 value in the test are better than the non-knowledge enhancement prompt method by 30 %. It can effectively deal with practical challenges such as vague symptom description and heterogeneous department names, and provide reliable support for intelligent pre-examination and triage.

  Key words: Medical Knowledge Graph; Knowledge Enhancement; Large Language Model; Intelligent Triage

  Submit time: 6 August 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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• ID	Submit time	Number	Download
  1	2026-03-11	10.12201/bmr.202608.00014V1	Download

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