Construction and Optimization of an Intelligent Medical Triage Recommendation System Based on Large Language Models

DOI: https://doi.org/10.62517/jbdc.202601113

Author(s)

Yan Yang1,*, Wentao Zhang2

Affiliation(s)

1Computer School, Central China Normal University, Wuhan, China 2Information Center, Wuhan University, Wuhan, China *Corresponding Author

Abstract

To address service pressures arising from surging online medical consultations and enhance triage accuracy and knowledge recommendation efficiency, this paper constructs and optimizes an AI-driven medical triage recommendation system based on large language models. By designing a hybrid architecture augmented with medical knowledge graphs and implementing efficiency-oriented model pruning and high-efficiency training strategies, the system achieves intelligent understanding of patient chief complaints, precise triage, and relevant knowledge recommendations. Experimental results demonstrate that the optimized system exhibits significant improvements in key performance metrics including triage accuracy, response speed, and answer safety. It efficiently supports physician decision-making, alleviates repetitive consultation burdens, and provides reliable self-service inquiries for patients. This system holds positive implications for optimizing healthcare resource allocation and alleviating service pressures.

Keywords

Large Language Model; Intelligent Triage; Recommendation System; Knowledge Graph

References

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