Research on the"Four-Dimensional Integration"Model for Intelligent Clinical Decision Support Based on Traditional Chinese Medicine Classics
JIA Zihan
TONG Lin
ZHANG Fengxia
TIAN Siwei
GUO Zhuang
ZENG Ziling
NIU Qikai
ZHANG Huamin
LI Bing
Abstract：Traditional Chinese medicine(TCM)classics carry the theoretical knowledge and clinical experience of TCM,serving as a crucial foundation for clinical practice.Effectively utilizing this knowledge and transforming it into clinical decision support remains a key research challenge.With the advancement of information technology,the clinical translation of classical texts has evolved from digitization to knowledge-based and intelligent applications,demanding deeper integration between classical knowledge and clinical needs.This paper proposes a"four-dimensional integration"clinical decision support model based on TCM classics,which adopts a multi-dimensional approach focusing on etiology,pathogenesis,symptoms,and treatment principles,with syndrome-formula correspondence as its core objective.The model supports the deep integration and intelligent application of classical knowledge in modern clinical diagnosis and treatment.By constructing a symptom-centered multi-dimensional decision pathway,combined with ancient-modern terminology mapping,specialized knowledge bases,knowledge graphs,and artificial intelligence algorithms such as graph convolutional networks,the model enables the fusion of classical and contemporary knowledge,optimizes diagnostic and therapeutic pathways,and facilitates intelligent diagnostic support and formula recommendations.This approach improves the accuracy,standardization,and intelligence of classical text-assisted clinical decision-making,offering new strategies for leveraging TCM classics in the management of major diseases.
Keywords：TCM classicsTCM clinical decision support systemFour-dimensional integrationSyndrome-formula correspondenceKnowledge graphIntelligent diagnosisModernized TCMSmart healthcare
Publication Date：2025-12-28
Online Publishing Date：2026-01-07(First online date of this platform, not the publication date of the document)
Pages：7( 2118-2124 )